update project design
This commit is contained in:
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*.csv
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*.html
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suppressPackageStartupMessages({
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library(dplyr)
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library(tidyr)
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})
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# estimate_naive_vasopressor_mortality_effect() computes the first simple
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# mortality comparison for the early vasopressor teaching example.
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#
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# This function deliberately does NOT adjust for confounding.
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# It only compares observed 28-day mortality between eligible patients who did
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# and did not receive early vasopressors.
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#
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# Arguments:
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# - icu_data: a data frame or tibble with the columns created by
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# simulate_icu_cohort().
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#
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# Returns:
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# - A named list with four pieces:
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# - eligible_icu_patients: only the target-trial eligible patients.
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# - mortality_risks_by_early_vasopressor: observed 28-day mortality risk in
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# each treatment group.
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# - mortality_effect_estimates: naive risk difference and risk ratio.
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# - baseline_characteristics_by_early_vasopressor: compact baseline means by
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# observed treatment group.
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#
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# Example REPL use:
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#
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# source("R/simulate_icu_cohort.R")
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# icu_data <- simulate_icu_cohort(n_patients = 1000, seed = 20260531)
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# mortality_analysis <- estimate_naive_vasopressor_mortality_effect(icu_data)
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# mortality_analysis$mortality_effect_estimates
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#
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estimate_naive_vasopressor_mortality_effect <- function(icu_data) {
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eligible_icu_patients <- icu_data |>
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filter(eligible == 1)
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mortality_risks_by_early_vasopressor <- eligible_icu_patients |>
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group_by(early_vasopressor) |>
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summarize(
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patient_count = n(),
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mortality_risk_28d = mean(death_28d),
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.groups = "drop"
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) |>
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mutate(
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observed_treatment_group = factor(
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early_vasopressor,
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levels = c(0, 1),
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labels = c("No early vasopressor", "Early vasopressor")
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)
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) |>
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select(observed_treatment_group, patient_count, mortality_risk_28d)
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mortality_risk_early_vasopressor <- mortality_risks_by_early_vasopressor |>
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filter(observed_treatment_group == "Early vasopressor") |>
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pull(mortality_risk_28d)
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mortality_risk_no_early_vasopressor <- mortality_risks_by_early_vasopressor |>
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filter(observed_treatment_group == "No early vasopressor") |>
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pull(mortality_risk_28d)
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mortality_effect_estimates <- tibble(
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estimate = c(
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"Eligible patients",
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"Early vasopressor patients",
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"No early vasopressor patients",
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"28-day mortality risk, early vasopressor",
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"28-day mortality risk, no early vasopressor",
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"Naive risk difference",
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"Naive risk ratio"
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),
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value = c(
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nrow(eligible_icu_patients),
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sum(eligible_icu_patients$early_vasopressor == 1),
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sum(eligible_icu_patients$early_vasopressor == 0),
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mortality_risk_early_vasopressor,
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mortality_risk_no_early_vasopressor,
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mortality_risk_early_vasopressor - mortality_risk_no_early_vasopressor,
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mortality_risk_early_vasopressor / mortality_risk_no_early_vasopressor
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)
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)
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baseline_characteristics_by_early_vasopressor <- eligible_icu_patients |>
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group_by(early_vasopressor) |>
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summarize(
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mean_age = mean(age),
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mean_sofa_score = mean(sofa_score),
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mean_lactate = mean(lactate),
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mean_map = mean(map),
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.groups = "drop"
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)
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list(
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eligible_icu_patients = eligible_icu_patients,
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mortality_risks_by_early_vasopressor = mortality_risks_by_early_vasopressor,
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mortality_effect_estimates = mortality_effect_estimates,
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baseline_characteristics_by_early_vasopressor = baseline_characteristics_by_early_vasopressor
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)
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}
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+85
-2
@@ -1,58 +1,141 @@
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suppressPackageStartupMessages(library(dplyr))
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suppressPackageStartupMessages({
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library(dplyr)
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})
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# simulate_icu_cohort() is our first reusable project primitive.
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#
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# A "primitive" is a small function that does one useful job for the project.
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# Here, the job is to create one simulated ICU observational cohort that we can
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# reuse in scripts, notebooks, and later targets pipelines.
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#
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# Arguments:
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# - n_patients: how many ICU patients to simulate.
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# - seed: a number that makes the random simulation reproducible.
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#
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# Returns:
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# - A tibble with one row per ICU patient.
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# - The columns include baseline variables, eligibility indicators, observed
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# early vasopressor treatment, treatment timing, and 28-day mortality.
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#
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# Example REPL use:
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#
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# source("R/simulate_icu_cohort.R")
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# icu_data <- simulate_icu_cohort(n_patients = 5, seed = 1)
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# icu_data
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#
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# Example output shape:
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#
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# # A tibble: 5 x 13
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# patient_id age sex sofa_score lactate map suspected_sepsis ...
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# <int> <dbl> <chr> <dbl> <dbl> <dbl> <int> ...
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# 1 1 56 female 8 2.9 59 1 ...
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#
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simulate_icu_cohort <- function(n_patients = 1000, seed = 20260531) {
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# set.seed() fixes the random-number stream.
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# That means the same inputs produce the same simulated dataset each time.
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set.seed(seed)
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# tibble() creates a modern data frame.
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# Each argument below becomes a column.
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# Each column must have either one value or n_patients values.
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tibble(
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# seq_len(n_patients) creates patient IDs 1, 2, ..., n_patients.
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patient_id = seq_len(n_patients),
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# rnorm() draws from a normal distribution.
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# round() makes age whole-number-like.
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# pmin() and pmax() cap ages to a plausible ICU range.
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age = round(rnorm(n_patients, mean = 65, sd = 14)) |>
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pmin(95) |>
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pmax(18),
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sex = sample(c("female", "male"), size = n_patients, replace = TRUE, prob = c(0.45, 0.55)),
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# sample() draws categorical values.
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# replace = TRUE means each patient gets an independent draw.
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sex = sample(
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c("female", "male"),
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size = n_patients,
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replace = TRUE,
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prob = c(0.45, 0.55)
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),
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# rpois() draws count-like SOFA scores from a Poisson distribution.
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# pmin(20) keeps the simulated score within a plausible upper range.
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sofa_score = rpois(n_patients, lambda = 7) |>
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pmin(20),
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# rlnorm() creates right-skewed lactate values.
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# Clinical lab values often have this kind of skew.
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lactate = round(rlnorm(n_patients, meanlog = log(3), sdlog = 0.5), 1) |>
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pmin(15),
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# MAP is mean arterial pressure.
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# Lower MAP means more hypotension and greater shock severity.
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map = round(rnorm(n_patients, mean = 62, sd = 10)) |>
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pmin(95) |>
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pmax(35),
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# rbinom(..., size = 1) creates 0/1 indicators.
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# Here 1 means suspected sepsis is present at ICU admission.
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suspected_sepsis = rbinom(n_patients, size = 1, prob = 0.90)
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) |>
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# mutate() adds new columns or changes existing columns.
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# These columns depend on the baseline variables created above.
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mutate(
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# as.integer(TRUE) is 1 and as.integer(FALSE) is 0.
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hypotension_at_baseline = as.integer(map < 65),
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elevated_lactate_at_baseline = as.integer(lactate >= 2),
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# This is the simulated eligibility definition for the target trial.
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# A patient is eligible only if all three baseline criteria are true.
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eligible = as.integer(
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suspected_sepsis == 1 &
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hypotension_at_baseline == 1 &
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elevated_lactate_at_baseline == 1
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),
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# This is not a real clinical score.
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# It is a simulation device that makes sicker patients more likely to
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# receive early vasopressors and more likely to die.
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severity_score = 0.04 * (age - 65) +
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0.18 * (sofa_score - 7) +
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0.25 * (lactate - 3) -
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0.04 * (map - 62),
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# plogis() converts any real number into a probability between 0 and 1.
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# Higher severity_score gives a higher chance of early vasopressors.
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prob_early_vasopressor = plogis(-0.2 + severity_score),
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# This is the observed treatment group in the observational data.
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# It is not randomized; it depends on severity through the probability above.
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early_vasopressor = rbinom(n(), size = 1, prob = prob_early_vasopressor),
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# ifelse() chooses one value when the condition is TRUE and another when FALSE.
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# Early-treated patients get a time between 0 and 2 hours.
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# Not-early-treated patients get a time between 2 and 24 hours.
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time_to_vasopressor_hours = ifelse(
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early_vasopressor == 1,
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runif(n(), min = 0, max = 2),
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runif(n(), min = 2, max = 24)
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) |>
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round(2),
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# This linear predictor controls each patient's mortality risk.
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# Sicker patients have higher risk; early vasopressor has a modest
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# protective effect in the data-generating process.
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mortality_linear_predictor = -1.4 +
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0.03 * (age - 65) +
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0.20 * (sofa_score - 7) +
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0.28 * (lactate - 3) -
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0.05 * (map - 62) -
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0.25 * early_vasopressor,
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# Convert the mortality linear predictor into a probability, then draw
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# the observed 28-day death indicator.
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prob_death_28d = plogis(mortality_linear_predictor),
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death_28d = rbinom(n(), size = 1, prob = prob_death_28d)
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) |>
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# select() keeps the columns learners should analyze.
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# We intentionally drop helper columns like severity_score and probabilities.
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select(
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patient_id,
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age,
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File diff suppressed because it is too large
Load Diff
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/.quarto/
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**/*.quarto_ipynb
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File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,8 @@
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---
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title: "Target Trial Basics: Early Vasopressors in Septic Shock"
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format: html
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format:
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html:
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embed-resources: true
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execute:
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echo: true
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warning: false
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@@ -18,18 +20,20 @@ The clinical question is:
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## Setup
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```{r}
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library(dplyr)
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library(gt)
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library(gtsummary)
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library(readr)
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library(skimr)
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library(tibble)
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# library() attaches a package so its functions are available by name.
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# suppressPackageStartupMessages() keeps package startup text out of the report.
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suppressPackageStartupMessages({
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library(dplyr)
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library(gt)
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library(gtsummary)
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library(tibble)
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})
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# source() loads reusable project functions from the R/ folder.
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source("../R/simulate_icu_cohort.R")
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source("../R/estimate_naive_vasopressor_mortality_effect.R")
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```
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These packages make the routine analysis code easier to read.
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`dplyr` handles data manipulation, `readr` reads rectangular files, `skimr` gives quick data summaries, `gt` builds display tables, and `gtsummary` builds analytic summary tables.
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## Why A Target Trial?
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Observational ICU data are not randomized.
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@@ -41,14 +45,25 @@ The target trial framework asks us to describe the randomized trial we wish we h
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## Target Trial Protocol
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```{r}
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target_trial_protocol <- tribble(
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~component, ~definition,
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"Eligibility criteria", "ICU admission, suspected sepsis, hypotension, elevated lactate",
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"Time zero", "ICU admission",
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"Treatment strategy 1", "Start vasopressors within 2 hours",
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"Treatment strategy 2", "Do not start vasopressors within 2 hours",
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"Outcome", "Death within 28 days",
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"Causal contrast", "Risk difference and risk ratio"
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# tibble() creates a small rectangular dataset.
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# Each named argument becomes one column.
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target_trial_protocol <- tibble(
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component = c(
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"Eligibility criteria",
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"Time zero",
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"Treatment strategy 1",
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"Treatment strategy 2",
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"Outcome",
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"Estimand"
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),
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definition = c(
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"ICU admission, suspected sepsis, hypotension, elevated lactate",
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"ICU admission",
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"Start vasopressors within 2 hours",
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"Do not start vasopressors within 2 hours",
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"Death within 28 days",
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"Risk difference and risk ratio"
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)
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)
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target_trial_protocol |>
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@@ -60,112 +75,73 @@ target_trial_protocol |>
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)
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```
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`tribble()` creates a small tibble by typing the rows directly.
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## Simulate The Cohort
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`gt()` turns that tibble into a clearer presentation table.
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For this report, we call the reusable simulation function directly.
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## Load The Simulated Cohort
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This first version uses a CSV generated by `scripts/01_simulate_icu_data_base_r.R`.
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Using the same seed gives the same simulated cohort each time the notebook renders.
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```{r}
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icu_data <- read_csv("../data/icu_septic_shock_base_r.csv", show_col_types = FALSE)
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icu_data <- simulate_icu_cohort(n_patients = 1000, seed = 20260531)
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naive_mortality_analysis <- estimate_naive_vasopressor_mortality_effect(icu_data)
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eligible_icu_patients <- naive_mortality_analysis$eligible_icu_patients
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```
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`read_csv()` reads a rectangular CSV file into R.
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The object `icu_data` is a tibble, where each row is one ICU patient.
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## Inspect The Data
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## Cohort Overview
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```{r}
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icu_data |>
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slice_head(n = 6)
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```
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`slice_head()` prints the first few rows so we can inspect the structure before analyzing anything.
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```{r}
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skim(icu_data)
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```
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`skim()` gives a quick summary of variable types, missingness, and distributions.
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## Apply Eligibility Criteria
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```{r}
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eligible_data <- icu_data |>
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filter(eligible == 1)
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```
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`filter()` keeps rows that satisfy a condition.
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Here, we keep only patients who satisfy the simulated eligibility criteria.
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## Count Treatment Groups
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```{r}
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eligible_data |>
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count(early_vasopressor)
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```
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`count()` counts how many eligible patients were observed under each treatment group.
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In this first simplified dataset:
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- `1` means vasopressors started within 2 hours.
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- `0` means vasopressors were not started within 2 hours.
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## Estimate Naive Mortality Risks
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```{r}
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naive_risks <- eligible_data |>
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group_by(early_vasopressor) |>
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summarize(
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n_patients = n(),
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risk_death_28d = mean(death_28d),
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.groups = "drop"
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)
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naive_risks |>
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gt() |>
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tab_header(title = "Naive 28-Day Mortality Risk") |>
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cols_label(
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early_vasopressor = "Early vasopressor",
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n_patients = "Patients",
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risk_death_28d = "28-day mortality risk"
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) |>
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fmt_number(columns = risk_death_28d, decimals = 3)
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```
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Because `death_28d` is coded as 0 or 1, its mean is the proportion who died.
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This is a naive comparison because it does not yet adjust for the fact that treatment decisions depend on patient severity.
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## Estimate Naive Contrasts
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||||
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```{r}
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risk_early <- naive_risks |>
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filter(early_vasopressor == 1) |>
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pull(risk_death_28d)
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risk_not_early <- naive_risks |>
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filter(early_vasopressor == 0) |>
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pull(risk_death_28d)
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naive_contrasts <- tibble(
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measure = c("Risk difference", "Risk ratio"),
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# These summary counts describe how the full simulated cohort maps onto the
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# eligible target-trial cohort.
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cohort_overview <- tibble(
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measure = c(
|
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"Simulated ICU patients",
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"Eligible patients",
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"Early vasopressor patients",
|
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"Not early vasopressor patients"
|
||||
),
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value = c(
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risk_early - risk_not_early,
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risk_early / risk_not_early
|
||||
nrow(icu_data),
|
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nrow(eligible_icu_patients),
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||||
sum(eligible_icu_patients$early_vasopressor == 1),
|
||||
sum(eligible_icu_patients$early_vasopressor == 0)
|
||||
)
|
||||
)
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||||
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||||
naive_contrasts |>
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||||
cohort_overview |>
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||||
gt() |>
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||||
tab_header(title = "Naive Treatment Contrast") |>
|
||||
tab_header(title = "Cohort Overview") |>
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||||
cols_label(
|
||||
measure = "Measure",
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||||
value = "Patients"
|
||||
) |>
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||||
fmt_integer(columns = value)
|
||||
```
|
||||
|
||||
## Naive Mortality Risk
|
||||
|
||||
```{r}
|
||||
naive_mortality_analysis$mortality_risks_by_early_vasopressor |>
|
||||
gt() |>
|
||||
tab_header(title = "Naive 28-Day Mortality Risk") |>
|
||||
cols_label(
|
||||
observed_treatment_group = "Observed treatment group",
|
||||
patient_count = "Patients",
|
||||
mortality_risk_28d = "28-day mortality risk"
|
||||
) |>
|
||||
fmt_integer(columns = patient_count) |>
|
||||
fmt_number(columns = mortality_risk_28d, decimals = 3)
|
||||
```
|
||||
|
||||
This is a naive comparison because it does not yet adjust for the fact that treatment decisions depend on patient severity.
|
||||
|
||||
## Naive Mortality Effect Estimates
|
||||
|
||||
```{r}
|
||||
naive_mortality_analysis$mortality_effect_estimates |>
|
||||
gt() |>
|
||||
tab_header(title = "Naive Mortality Effect Estimates") |>
|
||||
cols_label(
|
||||
estimate = "Estimate",
|
||||
value = "Value"
|
||||
) |>
|
||||
fmt_number(columns = value, decimals = 3)
|
||||
@@ -175,10 +151,10 @@ The risk difference is an absolute difference in 28-day mortality risk.
|
||||
|
||||
The risk ratio is a relative comparison of 28-day mortality risk.
|
||||
|
||||
## Check Confounding By Severity
|
||||
## Baseline Severity By Treatment Group
|
||||
|
||||
```{r}
|
||||
eligible_data |>
|
||||
eligible_icu_patients |>
|
||||
mutate(
|
||||
early_vasopressor = factor(
|
||||
early_vasopressor,
|
||||
@@ -198,14 +174,12 @@ eligible_data |>
|
||||
add_overall()
|
||||
```
|
||||
|
||||
This compares baseline severity between the two treatment groups.
|
||||
Early vasopressor patients are generally sicker at baseline in this simulated observational cohort.
|
||||
|
||||
If early vasopressor patients have higher SOFA scores, higher lactate, or lower MAP, then the naive comparison mixes the treatment effect with baseline severity differences.
|
||||
|
||||
That problem is one reason we need target trial emulation methods rather than a simple treated-versus-untreated comparison.
|
||||
That means the naive comparison mixes the effect of treatment with baseline severity differences, which is why we need target trial emulation methods rather than a simple treated-versus-untreated comparison.
|
||||
|
||||
## Next Step
|
||||
|
||||
The next lesson should walk through `R/simulate_icu_cohort.R` line by line.
|
||||
|
||||
After that, we can use `scripts/02_naive_analysis_base_r.R` to compute the first naive association and then discuss why it is not yet a causal estimate.
|
||||
After that, we can extend the notebooks to show why the naive mortality effect estimates are biased and start building adjusted analyses in reusable `R/` functions.
|
||||
|
||||
-12
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-236
@@ -1,236 +0,0 @@
|
||||
/* quarto syntax highlight colors */
|
||||
:root {
|
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--quarto-hl-ot-color: #003B4F;
|
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|
||||
--quarto-hl-ss-color: #20794D;
|
||||
--quarto-hl-an-color: #5E5E5E;
|
||||
--quarto-hl-fu-color: #4758AB;
|
||||
--quarto-hl-st-color: #20794D;
|
||||
--quarto-hl-cf-color: #003B4F;
|
||||
--quarto-hl-op-color: #5E5E5E;
|
||||
--quarto-hl-er-color: #AD0000;
|
||||
--quarto-hl-bn-color: #AD0000;
|
||||
--quarto-hl-al-color: #AD0000;
|
||||
--quarto-hl-va-color: #111111;
|
||||
--quarto-hl-bu-color: inherit;
|
||||
--quarto-hl-ex-color: inherit;
|
||||
--quarto-hl-pp-color: #AD0000;
|
||||
--quarto-hl-in-color: #5E5E5E;
|
||||
--quarto-hl-vs-color: #20794D;
|
||||
--quarto-hl-wa-color: #5E5E5E;
|
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--quarto-hl-do-color: #5E5E5E;
|
||||
--quarto-hl-im-color: #00769E;
|
||||
--quarto-hl-ch-color: #20794D;
|
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--quarto-hl-dt-color: #AD0000;
|
||||
--quarto-hl-fl-color: #AD0000;
|
||||
--quarto-hl-co-color: #5E5E5E;
|
||||
--quarto-hl-cv-color: #5E5E5E;
|
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--quarto-hl-cn-color: #8f5902;
|
||||
--quarto-hl-sc-color: #5E5E5E;
|
||||
--quarto-hl-dv-color: #AD0000;
|
||||
--quarto-hl-kw-color: #003B4F;
|
||||
}
|
||||
|
||||
/* other quarto variables */
|
||||
:root {
|
||||
--quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
|
||||
}
|
||||
|
||||
/* syntax highlight based on Pandoc's rules */
|
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pre > code.sourceCode > span {
|
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color: #003B4F;
|
||||
}
|
||||
|
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code.sourceCode > span {
|
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color: #003B4F;
|
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}
|
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|
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div.sourceCode,
|
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div.sourceCode pre.sourceCode {
|
||||
color: #003B4F;
|
||||
}
|
||||
|
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/* Normal */
|
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|
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color: #003B4F;
|
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}
|
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|
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/* Alert */
|
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code span.al {
|
||||
color: #AD0000;
|
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font-style: inherit;
|
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}
|
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|
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/* Annotation */
|
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code span.an {
|
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color: #5E5E5E;
|
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font-style: inherit;
|
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}
|
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|
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/* Attribute */
|
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code span.at {
|
||||
color: #657422;
|
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font-style: inherit;
|
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}
|
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|
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/* BaseN */
|
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code span.bn {
|
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color: #AD0000;
|
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font-style: inherit;
|
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}
|
||||
|
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/* BuiltIn */
|
||||
code span.bu {
|
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font-style: inherit;
|
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}
|
||||
|
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/* ControlFlow */
|
||||
code span.cf {
|
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color: #003B4F;
|
||||
font-weight: bold;
|
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font-style: inherit;
|
||||
}
|
||||
|
||||
/* Char */
|
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code span.ch {
|
||||
color: #20794D;
|
||||
font-style: inherit;
|
||||
}
|
||||
|
||||
/* Constant */
|
||||
code span.cn {
|
||||
color: #8f5902;
|
||||
font-style: inherit;
|
||||
}
|
||||
|
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/* Comment */
|
||||
code span.co {
|
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color: #5E5E5E;
|
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font-style: inherit;
|
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}
|
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|
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/* CommentVar */
|
||||
code span.cv {
|
||||
color: #5E5E5E;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
/* Documentation */
|
||||
code span.do {
|
||||
color: #5E5E5E;
|
||||
font-style: italic;
|
||||
}
|
||||
|
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/* DataType */
|
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code span.dt {
|
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|
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font-style: inherit;
|
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|
||||
|
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/* DecVal */
|
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|
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|
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font-style: inherit;
|
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}
|
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|
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/* Error */
|
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|
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|
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font-style: inherit;
|
||||
}
|
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|
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/* Extension */
|
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code span.ex {
|
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font-style: inherit;
|
||||
}
|
||||
|
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/* Float */
|
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code span.fl {
|
||||
color: #AD0000;
|
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font-style: inherit;
|
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}
|
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|
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/* Function */
|
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code span.fu {
|
||||
color: #4758AB;
|
||||
font-style: inherit;
|
||||
}
|
||||
|
||||
/* Import */
|
||||
code span.im {
|
||||
color: #00769E;
|
||||
font-style: inherit;
|
||||
}
|
||||
|
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/* Information */
|
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code span.in {
|
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color: #5E5E5E;
|
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font-style: inherit;
|
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|
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|
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/* Keyword */
|
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|
||||
color: #003B4F;
|
||||
font-weight: bold;
|
||||
font-style: inherit;
|
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|
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|
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/* Operator */
|
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code span.op {
|
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color: #5E5E5E;
|
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font-style: inherit;
|
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}
|
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|
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/* Other */
|
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code span.ot {
|
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color: #003B4F;
|
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font-style: inherit;
|
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|
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/* Preprocessor */
|
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|
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|
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font-style: inherit;
|
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|
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/* SpecialChar */
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code span.sc {
|
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color: #5E5E5E;
|
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font-style: inherit;
|
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|
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|
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/* SpecialString */
|
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code span.ss {
|
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color: #20794D;
|
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font-style: inherit;
|
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|
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|
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/* String */
|
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code span.st {
|
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color: #20794D;
|
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font-style: inherit;
|
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}
|
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|
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/* Variable */
|
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code span.va {
|
||||
color: #111111;
|
||||
font-style: inherit;
|
||||
}
|
||||
|
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/* VerbatimString */
|
||||
code span.vs {
|
||||
color: #20794D;
|
||||
font-style: inherit;
|
||||
}
|
||||
|
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/* Warning */
|
||||
code span.wa {
|
||||
color: #5E5E5E;
|
||||
font-style: italic;
|
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}
|
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|
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|
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content: "</";
|
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}
|
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|
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/*# sourceMappingURL=789ad8cd35626ba772517505b42a116e.css.map */
|
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@@ -1,845 +0,0 @@
|
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import * as tabsets from "./tabsets/tabsets.js";
|
||||
|
||||
const sectionChanged = new CustomEvent("quarto-sectionChanged", {
|
||||
detail: {},
|
||||
bubbles: true,
|
||||
cancelable: false,
|
||||
composed: false,
|
||||
});
|
||||
|
||||
const layoutMarginEls = () => {
|
||||
// Find any conflicting margin elements and add margins to the
|
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// top to prevent overlap
|
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const marginChildren = window.document.querySelectorAll(
|
||||
".column-margin.column-container > *, .margin-caption, .aside"
|
||||
);
|
||||
|
||||
let lastBottom = 0;
|
||||
for (const marginChild of marginChildren) {
|
||||
if (marginChild.offsetParent !== null) {
|
||||
// clear the top margin so we recompute it
|
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marginChild.style.marginTop = null;
|
||||
const top = marginChild.getBoundingClientRect().top + window.scrollY;
|
||||
if (top < lastBottom) {
|
||||
const marginChildStyle = window.getComputedStyle(marginChild);
|
||||
const marginBottom = parseFloat(marginChildStyle["marginBottom"]);
|
||||
const margin = lastBottom - top + marginBottom;
|
||||
marginChild.style.marginTop = `${margin}px`;
|
||||
}
|
||||
const styles = window.getComputedStyle(marginChild);
|
||||
const marginTop = parseFloat(styles["marginTop"]);
|
||||
lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
window.document.addEventListener("DOMContentLoaded", function (_event) {
|
||||
// Recompute the position of margin elements anytime the body size changes
|
||||
if (window.ResizeObserver) {
|
||||
const resizeObserver = new window.ResizeObserver(
|
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throttle(() => {
|
||||
layoutMarginEls();
|
||||
if (
|
||||
window.document.body.getBoundingClientRect().width < 990 &&
|
||||
isReaderMode()
|
||||
) {
|
||||
quartoToggleReader();
|
||||
}
|
||||
}, 50)
|
||||
);
|
||||
resizeObserver.observe(window.document.body);
|
||||
}
|
||||
|
||||
const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
|
||||
const sidebarEl = window.document.getElementById("quarto-sidebar");
|
||||
const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
|
||||
const marginSidebarEl = window.document.getElementById(
|
||||
"quarto-margin-sidebar"
|
||||
);
|
||||
// function to determine whether the element has a previous sibling that is active
|
||||
const prevSiblingIsActiveLink = (el) => {
|
||||
const sibling = el.previousElementSibling;
|
||||
if (sibling && sibling.tagName === "A") {
|
||||
return sibling.classList.contains("active");
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
// dispatch for htmlwidgets
|
||||
// they use slideenter event to trigger resize
|
||||
function fireSlideEnter() {
|
||||
const event = window.document.createEvent("Event");
|
||||
event.initEvent("slideenter", true, true);
|
||||
window.document.dispatchEvent(event);
|
||||
}
|
||||
|
||||
const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
|
||||
tabs.forEach((tab) => {
|
||||
tab.addEventListener("shown.bs.tab", fireSlideEnter);
|
||||
});
|
||||
|
||||
// dispatch for shiny
|
||||
// they use BS shown and hidden events to trigger rendering
|
||||
function distpatchShinyEvents(previous, current) {
|
||||
if (window.jQuery) {
|
||||
if (previous) {
|
||||
window.jQuery(previous).trigger("hidden");
|
||||
}
|
||||
if (current) {
|
||||
window.jQuery(current).trigger("shown");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// tabby.js listener: Trigger event for htmlwidget and shiny
|
||||
document.addEventListener(
|
||||
"tabby",
|
||||
function (event) {
|
||||
fireSlideEnter();
|
||||
distpatchShinyEvents(event.detail.previousTab, event.detail.tab);
|
||||
},
|
||||
false
|
||||
);
|
||||
|
||||
// Track scrolling and mark TOC links as active
|
||||
// get table of contents and sidebar (bail if we don't have at least one)
|
||||
const tocLinks = tocEl
|
||||
? [...tocEl.querySelectorAll("a[data-scroll-target]")]
|
||||
: [];
|
||||
const makeActive = (link) => tocLinks[link].classList.add("active");
|
||||
const removeActive = (link) => tocLinks[link].classList.remove("active");
|
||||
const removeAllActive = () =>
|
||||
[...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
|
||||
|
||||
// activate the anchor for a section associated with this TOC entry
|
||||
tocLinks.forEach((link) => {
|
||||
link.addEventListener("click", () => {
|
||||
if (link.href.indexOf("#") !== -1) {
|
||||
const anchor = link.href.split("#")[1];
|
||||
const heading = window.document.querySelector(
|
||||
`[data-anchor-id="${anchor}"]`
|
||||
);
|
||||
if (heading) {
|
||||
// Add the class
|
||||
heading.classList.add("reveal-anchorjs-link");
|
||||
|
||||
// function to show the anchor
|
||||
const handleMouseout = () => {
|
||||
heading.classList.remove("reveal-anchorjs-link");
|
||||
heading.removeEventListener("mouseout", handleMouseout);
|
||||
};
|
||||
|
||||
// add a function to clear the anchor when the user mouses out of it
|
||||
heading.addEventListener("mouseout", handleMouseout);
|
||||
}
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
const sections = tocLinks.map((link) => {
|
||||
const target = link.getAttribute("data-scroll-target");
|
||||
if (target.startsWith("#")) {
|
||||
return window.document.getElementById(decodeURI(`${target.slice(1)}`));
|
||||
} else {
|
||||
return window.document.querySelector(decodeURI(`${target}`));
|
||||
}
|
||||
});
|
||||
|
||||
const sectionMargin = 200;
|
||||
let currentActive = 0;
|
||||
// track whether we've initialized state the first time
|
||||
let init = false;
|
||||
|
||||
const updateActiveLink = () => {
|
||||
// The index from bottom to top (e.g. reversed list)
|
||||
let sectionIndex = -1;
|
||||
if (
|
||||
window.innerHeight + window.pageYOffset >=
|
||||
window.document.body.offsetHeight
|
||||
) {
|
||||
// This is the no-scroll case where last section should be the active one
|
||||
sectionIndex = 0;
|
||||
} else {
|
||||
// This finds the last section visible on screen that should be made active
|
||||
sectionIndex = [...sections].reverse().findIndex((section) => {
|
||||
if (section) {
|
||||
return window.pageYOffset >= section.offsetTop - sectionMargin;
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
});
|
||||
}
|
||||
if (sectionIndex > -1) {
|
||||
const current = sections.length - sectionIndex - 1;
|
||||
if (current !== currentActive) {
|
||||
removeAllActive();
|
||||
currentActive = current;
|
||||
makeActive(current);
|
||||
if (init) {
|
||||
window.dispatchEvent(sectionChanged);
|
||||
}
|
||||
init = true;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const inHiddenRegion = (top, bottom, hiddenRegions) => {
|
||||
for (const region of hiddenRegions) {
|
||||
if (top <= region.bottom && bottom >= region.top) {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
return false;
|
||||
};
|
||||
|
||||
const categorySelector = "header.quarto-title-block .quarto-category";
|
||||
const activateCategories = (href) => {
|
||||
// Find any categories
|
||||
// Surround them with a link pointing back to:
|
||||
// #category=Authoring
|
||||
try {
|
||||
const categoryEls = window.document.querySelectorAll(categorySelector);
|
||||
for (const categoryEl of categoryEls) {
|
||||
const categoryText = categoryEl.textContent;
|
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if (categoryText) {
|
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const link = `${href}#category=${encodeURIComponent(categoryText)}`;
|
||||
const linkEl = window.document.createElement("a");
|
||||
linkEl.setAttribute("href", link);
|
||||
for (const child of categoryEl.childNodes) {
|
||||
linkEl.append(child);
|
||||
}
|
||||
categoryEl.appendChild(linkEl);
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
// Ignore errors
|
||||
}
|
||||
};
|
||||
function hasTitleCategories() {
|
||||
return window.document.querySelector(categorySelector) !== null;
|
||||
}
|
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|
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function offsetRelativeUrl(url) {
|
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const offset = getMeta("quarto:offset");
|
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return offset ? offset + url : url;
|
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}
|
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|
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function offsetAbsoluteUrl(url) {
|
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const offset = getMeta("quarto:offset");
|
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const baseUrl = new URL(offset, window.location);
|
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|
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const projRelativeUrl = url.replace(baseUrl, "");
|
||||
if (projRelativeUrl.startsWith("/")) {
|
||||
return projRelativeUrl;
|
||||
} else {
|
||||
return "/" + projRelativeUrl;
|
||||
}
|
||||
}
|
||||
|
||||
// read a meta tag value
|
||||
function getMeta(metaName) {
|
||||
const metas = window.document.getElementsByTagName("meta");
|
||||
for (let i = 0; i < metas.length; i++) {
|
||||
if (metas[i].getAttribute("name") === metaName) {
|
||||
return metas[i].getAttribute("content");
|
||||
}
|
||||
}
|
||||
return "";
|
||||
}
|
||||
|
||||
async function findAndActivateCategories() {
|
||||
// Categories search with listing only use path without query
|
||||
const currentPagePath = offsetAbsoluteUrl(
|
||||
window.location.origin + window.location.pathname
|
||||
);
|
||||
const response = await fetch(offsetRelativeUrl("listings.json"));
|
||||
if (response.status == 200) {
|
||||
return response.json().then(function (listingPaths) {
|
||||
const listingHrefs = [];
|
||||
for (const listingPath of listingPaths) {
|
||||
const pathWithoutLeadingSlash = listingPath.listing.substring(1);
|
||||
for (const item of listingPath.items) {
|
||||
const encodedItem = encodeURI(item);
|
||||
if (
|
||||
encodedItem === currentPagePath ||
|
||||
encodedItem === currentPagePath + "index.html"
|
||||
) {
|
||||
// Resolve this path against the offset to be sure
|
||||
// we already are using the correct path to the listing
|
||||
// (this adjusts the listing urls to be rooted against
|
||||
// whatever root the page is actually running against)
|
||||
const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
|
||||
const baseUrl = window.location;
|
||||
const resolvedPath = new URL(relative, baseUrl);
|
||||
listingHrefs.push(resolvedPath.pathname);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Look up the tree for a nearby linting and use that if we find one
|
||||
const nearestListing = findNearestParentListing(
|
||||
offsetAbsoluteUrl(window.location.pathname),
|
||||
listingHrefs
|
||||
);
|
||||
if (nearestListing) {
|
||||
activateCategories(nearestListing);
|
||||
} else {
|
||||
// See if the referrer is a listing page for this item
|
||||
const referredRelativePath = offsetAbsoluteUrl(document.referrer);
|
||||
const referrerListing = listingHrefs.find((listingHref) => {
|
||||
const isListingReferrer =
|
||||
listingHref === referredRelativePath ||
|
||||
listingHref === referredRelativePath + "index.html";
|
||||
return isListingReferrer;
|
||||
});
|
||||
|
||||
if (referrerListing) {
|
||||
// Try to use the referrer if possible
|
||||
activateCategories(referrerListing);
|
||||
} else if (listingHrefs.length > 0) {
|
||||
// Otherwise, just fall back to the first listing
|
||||
activateCategories(listingHrefs[0]);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
if (hasTitleCategories()) {
|
||||
findAndActivateCategories();
|
||||
}
|
||||
|
||||
const findNearestParentListing = (href, listingHrefs) => {
|
||||
if (!href || !listingHrefs) {
|
||||
return undefined;
|
||||
}
|
||||
// Look up the tree for a nearby linting and use that if we find one
|
||||
const relativeParts = href.substring(1).split("/");
|
||||
while (relativeParts.length > 0) {
|
||||
const path = relativeParts.join("/");
|
||||
for (const listingHref of listingHrefs) {
|
||||
if (listingHref.startsWith(path)) {
|
||||
return listingHref;
|
||||
}
|
||||
}
|
||||
relativeParts.pop();
|
||||
}
|
||||
|
||||
return undefined;
|
||||
};
|
||||
|
||||
const manageSidebarVisiblity = (el, placeholderDescriptor) => {
|
||||
let isVisible = true;
|
||||
let elRect;
|
||||
|
||||
return (hiddenRegions) => {
|
||||
if (el === null) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Find the last element of the TOC
|
||||
const lastChildEl = el.lastElementChild;
|
||||
|
||||
if (lastChildEl) {
|
||||
// Converts the sidebar to a menu
|
||||
const convertToMenu = () => {
|
||||
for (const child of el.children) {
|
||||
child.style.opacity = 0;
|
||||
child.style.overflow = "hidden";
|
||||
child.style.pointerEvents = "none";
|
||||
}
|
||||
|
||||
nexttick(() => {
|
||||
const toggleContainer = window.document.createElement("div");
|
||||
toggleContainer.style.width = "100%";
|
||||
toggleContainer.classList.add("zindex-over-content");
|
||||
toggleContainer.classList.add("quarto-sidebar-toggle");
|
||||
toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
|
||||
toggleContainer.id = placeholderDescriptor.id;
|
||||
toggleContainer.style.position = "fixed";
|
||||
|
||||
const toggleIcon = window.document.createElement("i");
|
||||
toggleIcon.classList.add("quarto-sidebar-toggle-icon");
|
||||
toggleIcon.classList.add("bi");
|
||||
toggleIcon.classList.add("bi-caret-down-fill");
|
||||
|
||||
const toggleTitle = window.document.createElement("div");
|
||||
const titleEl = window.document.body.querySelector(
|
||||
placeholderDescriptor.titleSelector
|
||||
);
|
||||
if (titleEl) {
|
||||
toggleTitle.append(
|
||||
titleEl.textContent || titleEl.innerText,
|
||||
toggleIcon
|
||||
);
|
||||
}
|
||||
toggleTitle.classList.add("zindex-over-content");
|
||||
toggleTitle.classList.add("quarto-sidebar-toggle-title");
|
||||
toggleContainer.append(toggleTitle);
|
||||
|
||||
const toggleContents = window.document.createElement("div");
|
||||
toggleContents.classList = el.classList;
|
||||
toggleContents.classList.add("zindex-over-content");
|
||||
toggleContents.classList.add("quarto-sidebar-toggle-contents");
|
||||
for (const child of el.children) {
|
||||
if (child.id === "toc-title") {
|
||||
continue;
|
||||
}
|
||||
|
||||
const clone = child.cloneNode(true);
|
||||
clone.style.opacity = 1;
|
||||
clone.style.pointerEvents = null;
|
||||
clone.style.display = null;
|
||||
toggleContents.append(clone);
|
||||
}
|
||||
toggleContents.style.height = "0px";
|
||||
const positionToggle = () => {
|
||||
// position the element (top left of parent, same width as parent)
|
||||
if (!elRect) {
|
||||
elRect = el.getBoundingClientRect();
|
||||
}
|
||||
toggleContainer.style.left = `${elRect.left}px`;
|
||||
toggleContainer.style.top = `${elRect.top}px`;
|
||||
toggleContainer.style.width = `${elRect.width}px`;
|
||||
};
|
||||
positionToggle();
|
||||
|
||||
toggleContainer.append(toggleContents);
|
||||
el.parentElement.prepend(toggleContainer);
|
||||
|
||||
// Process clicks
|
||||
let tocShowing = false;
|
||||
// Allow the caller to control whether this is dismissed
|
||||
// when it is clicked (e.g. sidebar navigation supports
|
||||
// opening and closing the nav tree, so don't dismiss on click)
|
||||
const clickEl = placeholderDescriptor.dismissOnClick
|
||||
? toggleContainer
|
||||
: toggleTitle;
|
||||
|
||||
const closeToggle = () => {
|
||||
if (tocShowing) {
|
||||
toggleContainer.classList.remove("expanded");
|
||||
toggleContents.style.height = "0px";
|
||||
tocShowing = false;
|
||||
}
|
||||
};
|
||||
|
||||
// Get rid of any expanded toggle if the user scrolls
|
||||
window.document.addEventListener(
|
||||
"scroll",
|
||||
throttle(() => {
|
||||
closeToggle();
|
||||
}, 50)
|
||||
);
|
||||
|
||||
// Handle positioning of the toggle
|
||||
window.addEventListener(
|
||||
"resize",
|
||||
throttle(() => {
|
||||
elRect = undefined;
|
||||
positionToggle();
|
||||
}, 50)
|
||||
);
|
||||
|
||||
window.addEventListener("quarto-hrChanged", () => {
|
||||
elRect = undefined;
|
||||
});
|
||||
|
||||
// Process the click
|
||||
clickEl.onclick = () => {
|
||||
if (!tocShowing) {
|
||||
toggleContainer.classList.add("expanded");
|
||||
toggleContents.style.height = null;
|
||||
tocShowing = true;
|
||||
} else {
|
||||
closeToggle();
|
||||
}
|
||||
};
|
||||
});
|
||||
};
|
||||
|
||||
// Converts a sidebar from a menu back to a sidebar
|
||||
const convertToSidebar = () => {
|
||||
for (const child of el.children) {
|
||||
child.style.opacity = 1;
|
||||
child.style.overflow = null;
|
||||
child.style.pointerEvents = null;
|
||||
}
|
||||
|
||||
const placeholderEl = window.document.getElementById(
|
||||
placeholderDescriptor.id
|
||||
);
|
||||
if (placeholderEl) {
|
||||
placeholderEl.remove();
|
||||
}
|
||||
|
||||
el.classList.remove("rollup");
|
||||
};
|
||||
|
||||
if (isReaderMode()) {
|
||||
convertToMenu();
|
||||
isVisible = false;
|
||||
} else {
|
||||
// Find the top and bottom o the element that is being managed
|
||||
const elTop = el.offsetTop;
|
||||
const elBottom =
|
||||
elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
|
||||
|
||||
if (!isVisible) {
|
||||
// If the element is current not visible reveal if there are
|
||||
// no conflicts with overlay regions
|
||||
if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
|
||||
convertToSidebar();
|
||||
isVisible = true;
|
||||
}
|
||||
} else {
|
||||
// If the element is visible, hide it if it conflicts with overlay regions
|
||||
// and insert a placeholder toggle (or if we're in reader mode)
|
||||
if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
|
||||
convertToMenu();
|
||||
isVisible = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
|
||||
for (const tabEl of tabEls) {
|
||||
const id = tabEl.getAttribute("data-bs-target");
|
||||
if (id) {
|
||||
const columnEl = document.querySelector(
|
||||
`${id} .column-margin, .tabset-margin-content`
|
||||
);
|
||||
if (columnEl)
|
||||
tabEl.addEventListener("shown.bs.tab", function (event) {
|
||||
const el = event.srcElement;
|
||||
if (el) {
|
||||
const visibleCls = `${el.id}-margin-content`;
|
||||
// walk up until we find a parent tabset
|
||||
let panelTabsetEl = el.parentElement;
|
||||
while (panelTabsetEl) {
|
||||
if (panelTabsetEl.classList.contains("panel-tabset")) {
|
||||
break;
|
||||
}
|
||||
panelTabsetEl = panelTabsetEl.parentElement;
|
||||
}
|
||||
|
||||
if (panelTabsetEl) {
|
||||
const prevSib = panelTabsetEl.previousElementSibling;
|
||||
if (
|
||||
prevSib &&
|
||||
prevSib.classList.contains("tabset-margin-container")
|
||||
) {
|
||||
const childNodes = prevSib.querySelectorAll(
|
||||
".tabset-margin-content"
|
||||
);
|
||||
for (const childEl of childNodes) {
|
||||
if (childEl.classList.contains(visibleCls)) {
|
||||
childEl.classList.remove("collapse");
|
||||
} else {
|
||||
childEl.classList.add("collapse");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
layoutMarginEls();
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Manage the visibility of the toc and the sidebar
|
||||
const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
|
||||
id: "quarto-toc-toggle",
|
||||
titleSelector: "#toc-title",
|
||||
dismissOnClick: true,
|
||||
});
|
||||
const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
|
||||
id: "quarto-sidebarnav-toggle",
|
||||
titleSelector: ".title",
|
||||
dismissOnClick: false,
|
||||
});
|
||||
let tocLeftScrollVisibility;
|
||||
if (leftTocEl) {
|
||||
tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
|
||||
id: "quarto-lefttoc-toggle",
|
||||
titleSelector: "#toc-title",
|
||||
dismissOnClick: true,
|
||||
});
|
||||
}
|
||||
|
||||
// Find the first element that uses formatting in special columns
|
||||
const conflictingEls = window.document.body.querySelectorAll(
|
||||
'[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
|
||||
);
|
||||
|
||||
// Filter all the possibly conflicting elements into ones
|
||||
// the do conflict on the left or ride side
|
||||
const arrConflictingEls = Array.from(conflictingEls);
|
||||
const leftSideConflictEls = arrConflictingEls.filter((el) => {
|
||||
if (el.tagName === "ASIDE") {
|
||||
return false;
|
||||
}
|
||||
return Array.from(el.classList).find((className) => {
|
||||
return (
|
||||
className !== "column-body" &&
|
||||
className.startsWith("column-") &&
|
||||
!className.endsWith("right") &&
|
||||
!className.endsWith("container") &&
|
||||
className !== "column-margin"
|
||||
);
|
||||
});
|
||||
});
|
||||
const rightSideConflictEls = arrConflictingEls.filter((el) => {
|
||||
if (el.tagName === "ASIDE") {
|
||||
return true;
|
||||
}
|
||||
|
||||
const hasMarginCaption = Array.from(el.classList).find((className) => {
|
||||
return className == "margin-caption";
|
||||
});
|
||||
if (hasMarginCaption) {
|
||||
return true;
|
||||
}
|
||||
|
||||
return Array.from(el.classList).find((className) => {
|
||||
return (
|
||||
className !== "column-body" &&
|
||||
!className.endsWith("container") &&
|
||||
className.startsWith("column-") &&
|
||||
!className.endsWith("left")
|
||||
);
|
||||
});
|
||||
});
|
||||
|
||||
const kOverlapPaddingSize = 10;
|
||||
function toRegions(els) {
|
||||
return els.map((el) => {
|
||||
const boundRect = el.getBoundingClientRect();
|
||||
const top =
|
||||
boundRect.top +
|
||||
document.documentElement.scrollTop -
|
||||
kOverlapPaddingSize;
|
||||
return {
|
||||
top,
|
||||
bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
let hasObserved = false;
|
||||
const visibleItemObserver = (els) => {
|
||||
let visibleElements = [...els];
|
||||
const intersectionObserver = new IntersectionObserver(
|
||||
(entries, _observer) => {
|
||||
entries.forEach((entry) => {
|
||||
if (entry.isIntersecting) {
|
||||
if (visibleElements.indexOf(entry.target) === -1) {
|
||||
visibleElements.push(entry.target);
|
||||
}
|
||||
} else {
|
||||
visibleElements = visibleElements.filter((visibleEntry) => {
|
||||
return visibleEntry !== entry;
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
if (!hasObserved) {
|
||||
hideOverlappedSidebars();
|
||||
}
|
||||
hasObserved = true;
|
||||
},
|
||||
{}
|
||||
);
|
||||
els.forEach((el) => {
|
||||
intersectionObserver.observe(el);
|
||||
});
|
||||
|
||||
return {
|
||||
getVisibleEntries: () => {
|
||||
return visibleElements;
|
||||
},
|
||||
};
|
||||
};
|
||||
|
||||
const rightElementObserver = visibleItemObserver(rightSideConflictEls);
|
||||
const leftElementObserver = visibleItemObserver(leftSideConflictEls);
|
||||
|
||||
const hideOverlappedSidebars = () => {
|
||||
marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
|
||||
sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
|
||||
if (tocLeftScrollVisibility) {
|
||||
tocLeftScrollVisibility(
|
||||
toRegions(leftElementObserver.getVisibleEntries())
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
window.quartoToggleReader = () => {
|
||||
// Applies a slow class (or removes it)
|
||||
// to update the transition speed
|
||||
const slowTransition = (slow) => {
|
||||
const manageTransition = (id, slow) => {
|
||||
const el = document.getElementById(id);
|
||||
if (el) {
|
||||
if (slow) {
|
||||
el.classList.add("slow");
|
||||
} else {
|
||||
el.classList.remove("slow");
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
manageTransition("TOC", slow);
|
||||
manageTransition("quarto-sidebar", slow);
|
||||
};
|
||||
const readerMode = !isReaderMode();
|
||||
setReaderModeValue(readerMode);
|
||||
|
||||
// If we're entering reader mode, slow the transition
|
||||
if (readerMode) {
|
||||
slowTransition(readerMode);
|
||||
}
|
||||
highlightReaderToggle(readerMode);
|
||||
hideOverlappedSidebars();
|
||||
|
||||
// If we're exiting reader mode, restore the non-slow transition
|
||||
if (!readerMode) {
|
||||
slowTransition(!readerMode);
|
||||
}
|
||||
};
|
||||
|
||||
const highlightReaderToggle = (readerMode) => {
|
||||
const els = document.querySelectorAll(".quarto-reader-toggle");
|
||||
if (els) {
|
||||
els.forEach((el) => {
|
||||
if (readerMode) {
|
||||
el.classList.add("reader");
|
||||
} else {
|
||||
el.classList.remove("reader");
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
const setReaderModeValue = (val) => {
|
||||
if (window.location.protocol !== "file:") {
|
||||
window.localStorage.setItem("quarto-reader-mode", val);
|
||||
} else {
|
||||
localReaderMode = val;
|
||||
}
|
||||
};
|
||||
|
||||
const isReaderMode = () => {
|
||||
if (window.location.protocol !== "file:") {
|
||||
return window.localStorage.getItem("quarto-reader-mode") === "true";
|
||||
} else {
|
||||
return localReaderMode;
|
||||
}
|
||||
};
|
||||
let localReaderMode = null;
|
||||
|
||||
const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
|
||||
const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
|
||||
|
||||
// Walk the TOC and collapse/expand nodes
|
||||
// Nodes are expanded if:
|
||||
// - they are top level
|
||||
// - they have children that are 'active' links
|
||||
// - they are directly below an link that is 'active'
|
||||
const walk = (el, depth) => {
|
||||
// Tick depth when we enter a UL
|
||||
if (el.tagName === "UL") {
|
||||
depth = depth + 1;
|
||||
}
|
||||
|
||||
// It this is active link
|
||||
let isActiveNode = false;
|
||||
if (el.tagName === "A" && el.classList.contains("active")) {
|
||||
isActiveNode = true;
|
||||
}
|
||||
|
||||
// See if there is an active child to this element
|
||||
let hasActiveChild = false;
|
||||
for (const child of el.children) {
|
||||
hasActiveChild = walk(child, depth) || hasActiveChild;
|
||||
}
|
||||
|
||||
// Process the collapse state if this is an UL
|
||||
if (el.tagName === "UL") {
|
||||
if (tocOpenDepth === -1 && depth > 1) {
|
||||
// toc-expand: false
|
||||
el.classList.add("collapse");
|
||||
} else if (
|
||||
depth <= tocOpenDepth ||
|
||||
hasActiveChild ||
|
||||
prevSiblingIsActiveLink(el)
|
||||
) {
|
||||
el.classList.remove("collapse");
|
||||
} else {
|
||||
el.classList.add("collapse");
|
||||
}
|
||||
|
||||
// untick depth when we leave a UL
|
||||
depth = depth - 1;
|
||||
}
|
||||
return hasActiveChild || isActiveNode;
|
||||
};
|
||||
|
||||
// walk the TOC and expand / collapse any items that should be shown
|
||||
if (tocEl) {
|
||||
updateActiveLink();
|
||||
walk(tocEl, 0);
|
||||
}
|
||||
|
||||
// Throttle the scroll event and walk peridiocally
|
||||
window.document.addEventListener(
|
||||
"scroll",
|
||||
throttle(() => {
|
||||
if (tocEl) {
|
||||
updateActiveLink();
|
||||
walk(tocEl, 0);
|
||||
}
|
||||
if (!isReaderMode()) {
|
||||
hideOverlappedSidebars();
|
||||
}
|
||||
}, 5)
|
||||
);
|
||||
window.addEventListener(
|
||||
"resize",
|
||||
throttle(() => {
|
||||
if (tocEl) {
|
||||
updateActiveLink();
|
||||
walk(tocEl, 0);
|
||||
}
|
||||
if (!isReaderMode()) {
|
||||
hideOverlappedSidebars();
|
||||
}
|
||||
}, 10)
|
||||
);
|
||||
hideOverlappedSidebars();
|
||||
highlightReaderToggle(isReaderMode());
|
||||
});
|
||||
|
||||
tabsets.init();
|
||||
|
||||
function throttle(func, wait) {
|
||||
let waiting = false;
|
||||
return function () {
|
||||
if (!waiting) {
|
||||
func.apply(this, arguments);
|
||||
waiting = true;
|
||||
setTimeout(function () {
|
||||
waiting = false;
|
||||
}, wait);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
function nexttick(func) {
|
||||
return setTimeout(func, 0);
|
||||
}
|
||||
@@ -1,95 +0,0 @@
|
||||
// grouped tabsets
|
||||
|
||||
export function init() {
|
||||
window.addEventListener("pageshow", (_event) => {
|
||||
function getTabSettings() {
|
||||
const data = localStorage.getItem("quarto-persistent-tabsets-data");
|
||||
if (!data) {
|
||||
localStorage.setItem("quarto-persistent-tabsets-data", "{}");
|
||||
return {};
|
||||
}
|
||||
if (data) {
|
||||
return JSON.parse(data);
|
||||
}
|
||||
}
|
||||
|
||||
function setTabSettings(data) {
|
||||
localStorage.setItem(
|
||||
"quarto-persistent-tabsets-data",
|
||||
JSON.stringify(data)
|
||||
);
|
||||
}
|
||||
|
||||
function setTabState(groupName, groupValue) {
|
||||
const data = getTabSettings();
|
||||
data[groupName] = groupValue;
|
||||
setTabSettings(data);
|
||||
}
|
||||
|
||||
function toggleTab(tab, active) {
|
||||
const tabPanelId = tab.getAttribute("aria-controls");
|
||||
const tabPanel = document.getElementById(tabPanelId);
|
||||
if (active) {
|
||||
tab.classList.add("active");
|
||||
tabPanel.classList.add("active");
|
||||
} else {
|
||||
tab.classList.remove("active");
|
||||
tabPanel.classList.remove("active");
|
||||
}
|
||||
}
|
||||
|
||||
function toggleAll(selectedGroup, selectorsToSync) {
|
||||
for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
|
||||
const active = selectedGroup === thisGroup;
|
||||
for (const tab of tabs) {
|
||||
toggleTab(tab, active);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function findSelectorsToSyncByLanguage() {
|
||||
const result = {};
|
||||
const tabs = Array.from(
|
||||
document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
|
||||
);
|
||||
for (const item of tabs) {
|
||||
const div = item.parentElement.parentElement.parentElement;
|
||||
const group = div.getAttribute("data-group");
|
||||
if (!result[group]) {
|
||||
result[group] = {};
|
||||
}
|
||||
const selectorsToSync = result[group];
|
||||
const value = item.innerHTML;
|
||||
if (!selectorsToSync[value]) {
|
||||
selectorsToSync[value] = [];
|
||||
}
|
||||
selectorsToSync[value].push(item);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
function setupSelectorSync() {
|
||||
const selectorsToSync = findSelectorsToSyncByLanguage();
|
||||
Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
|
||||
Object.entries(tabSetsByValue).forEach(([value, items]) => {
|
||||
items.forEach((item) => {
|
||||
item.addEventListener("click", (_event) => {
|
||||
setTabState(group, value);
|
||||
toggleAll(value, selectorsToSync[group]);
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
return selectorsToSync;
|
||||
}
|
||||
|
||||
const selectorsToSync = setupSelectorSync();
|
||||
for (const [group, selectedName] of Object.entries(getTabSettings())) {
|
||||
const selectors = selectorsToSync[group];
|
||||
// it's possible that stale state gives us empty selections, so we explicitly check here.
|
||||
if (selectors) {
|
||||
toggleAll(selectedName, selectors);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,164 @@
|
||||
---
|
||||
title: "Explore Simulated ICU Data"
|
||||
format:
|
||||
html:
|
||||
embed-resources: true
|
||||
execute:
|
||||
echo: true
|
||||
warning: false
|
||||
message: false
|
||||
---
|
||||
|
||||
## Goal
|
||||
|
||||
This notebook is for exploring the simulated ICU cohort.
|
||||
|
||||
The report notebook focuses on target trial components and core results. This exploratory notebook focuses on visualizing the data-generating process and understanding why the naive comparison can be biased.
|
||||
|
||||
## Setup
|
||||
|
||||
```{r}
|
||||
suppressPackageStartupMessages({
|
||||
library(dplyr)
|
||||
library(ggplot2)
|
||||
library(gt)
|
||||
library(gtsummary)
|
||||
library(tibble)
|
||||
})
|
||||
|
||||
source("../R/simulate_icu_cohort.R")
|
||||
source("../R/estimate_naive_vasopressor_mortality_effect.R")
|
||||
```
|
||||
|
||||
## Simulate Data
|
||||
|
||||
```{r}
|
||||
icu_data <- simulate_icu_cohort(n_patients = 1000, seed = 20260531)
|
||||
naive_mortality_analysis <- estimate_naive_vasopressor_mortality_effect(icu_data)
|
||||
eligible_icu_patients <- naive_mortality_analysis$eligible_icu_patients
|
||||
```
|
||||
|
||||
## Eligibility Overview
|
||||
|
||||
```{r}
|
||||
eligibility_overview <- icu_data |>
|
||||
summarize(
|
||||
simulated_patients = n(),
|
||||
suspected_sepsis = sum(suspected_sepsis == 1),
|
||||
hypotension_at_baseline = sum(hypotension_at_baseline == 1),
|
||||
elevated_lactate_at_baseline = sum(elevated_lactate_at_baseline == 1),
|
||||
eligible = sum(eligible == 1)
|
||||
) |>
|
||||
tidyr::pivot_longer(
|
||||
cols = everything(),
|
||||
names_to = "measure",
|
||||
values_to = "patients"
|
||||
)
|
||||
|
||||
eligibility_overview |>
|
||||
gt() |>
|
||||
tab_header(title = "Eligibility Overview") |>
|
||||
cols_label(
|
||||
measure = "Measure",
|
||||
patients = "Patients"
|
||||
) |>
|
||||
fmt_integer(columns = patients)
|
||||
```
|
||||
|
||||
## Baseline Distributions
|
||||
|
||||
```{r}
|
||||
eligible_icu_patients |>
|
||||
select(age, sofa_score, lactate, map) |>
|
||||
tbl_summary(
|
||||
statistic = all_continuous() ~ "{mean} ({sd}); {median} [{p25}, {p75}]",
|
||||
missing = "no"
|
||||
)
|
||||
```
|
||||
|
||||
## Severity By Observed Treatment
|
||||
|
||||
```{r}
|
||||
eligible_icu_patients |>
|
||||
mutate(
|
||||
observed_treatment = factor(
|
||||
early_vasopressor,
|
||||
levels = c(0, 1),
|
||||
labels = c("No early vasopressor", "Early vasopressor")
|
||||
)
|
||||
) |>
|
||||
ggplot(aes(x = observed_treatment, y = sofa_score, fill = observed_treatment)) +
|
||||
geom_boxplot(alpha = 0.75, width = 0.65, show.legend = FALSE) +
|
||||
labs(
|
||||
title = "SOFA Score Is Higher In Early-Treated Patients",
|
||||
x = NULL,
|
||||
y = "SOFA score"
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
Early vasopressor patients tend to have higher SOFA scores in this simulated cohort. That happens because the treatment assignment mechanism makes sicker patients more likely to receive early vasopressors.
|
||||
|
||||
## Lactate And MAP By Treatment
|
||||
|
||||
```{r}
|
||||
eligible_icu_patients |>
|
||||
mutate(
|
||||
observed_treatment = factor(
|
||||
early_vasopressor,
|
||||
levels = c(0, 1),
|
||||
labels = c("No early vasopressor", "Early vasopressor")
|
||||
)
|
||||
) |>
|
||||
ggplot(aes(x = map, y = lactate, color = observed_treatment)) +
|
||||
geom_point(alpha = 0.55) +
|
||||
labs(
|
||||
title = "Shock Severity Markers Differ By Observed Treatment",
|
||||
x = "Mean arterial pressure",
|
||||
y = "Lactate",
|
||||
color = "Observed treatment"
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
Higher lactate and lower MAP are both markers of greater shock severity. If treatment groups differ on these variables, a simple treated-versus-untreated comparison is not yet a causal estimate.
|
||||
|
||||
## Mortality By SOFA Score
|
||||
|
||||
```{r}
|
||||
eligible_icu_patients |>
|
||||
mutate(
|
||||
observed_treatment = factor(
|
||||
early_vasopressor,
|
||||
levels = c(0, 1),
|
||||
labels = c("No early vasopressor", "Early vasopressor")
|
||||
)
|
||||
) |>
|
||||
ggplot(aes(x = sofa_score, y = death_28d, color = observed_treatment)) +
|
||||
geom_jitter(height = 0.04, width = 0.15, alpha = 0.35) +
|
||||
geom_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE) +
|
||||
labs(
|
||||
title = "Mortality Risk Rises With SOFA Score",
|
||||
x = "SOFA score",
|
||||
y = "Observed 28-day death",
|
||||
color = "Observed treatment"
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
This plot shows why baseline severity matters. If SOFA score predicts death and also affects treatment assignment, then SOFA score is a confounder for the naive treatment comparison.
|
||||
|
||||
## Naive Mortality Effect Estimates
|
||||
|
||||
```{r}
|
||||
naive_mortality_analysis$mortality_effect_estimates |>
|
||||
gt() |>
|
||||
tab_header(title = "Naive 28-Day Mortality Effect Estimates") |>
|
||||
cols_label(
|
||||
estimate = "Estimate",
|
||||
value = "Value"
|
||||
) |>
|
||||
fmt_number(columns = value, decimals = 3)
|
||||
```
|
||||
|
||||
The next methodological step is to adjust for baseline severity rather than comparing observed treatment groups directly.
|
||||
@@ -1,513 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8"/>
|
||||
<style>body{background-color:white;}</style>
|
||||
|
||||
|
||||
</head>
|
||||
<body>
|
||||
<div id="aphfsmpepd" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
|
||||
<style>#aphfsmpepd table {
|
||||
font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
}
|
||||
|
||||
#aphfsmpepd thead, #aphfsmpepd tbody, #aphfsmpepd tfoot, #aphfsmpepd tr, #aphfsmpepd td, #aphfsmpepd th {
|
||||
border-style: none;
|
||||
}
|
||||
|
||||
#aphfsmpepd p {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_table {
|
||||
display: table;
|
||||
border-collapse: collapse;
|
||||
line-height: normal;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
color: #333333;
|
||||
font-size: 16px;
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
background-color: #FFFFFF;
|
||||
width: auto;
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #A8A8A8;
|
||||
border-right-style: none;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #A8A8A8;
|
||||
border-left-style: none;
|
||||
border-left-width: 2px;
|
||||
border-left-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_caption {
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_title {
|
||||
color: #333333;
|
||||
font-size: 125%;
|
||||
font-weight: initial;
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-bottom-color: #FFFFFF;
|
||||
border-bottom-width: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_subtitle {
|
||||
color: #333333;
|
||||
font-size: 85%;
|
||||
font-weight: initial;
|
||||
padding-top: 3px;
|
||||
padding-bottom: 5px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-top-color: #FFFFFF;
|
||||
border-top-width: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_heading {
|
||||
background-color: #FFFFFF;
|
||||
text-align: center;
|
||||
border-bottom-color: #FFFFFF;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_bottom_border {
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_col_headings {
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_col_heading {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: normal;
|
||||
text-transform: inherit;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
vertical-align: bottom;
|
||||
padding-top: 5px;
|
||||
padding-bottom: 6px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
overflow-x: hidden;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_column_spanner_outer {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: normal;
|
||||
text-transform: inherit;
|
||||
padding-top: 0;
|
||||
padding-bottom: 0;
|
||||
padding-left: 4px;
|
||||
padding-right: 4px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_column_spanner_outer:first-child {
|
||||
padding-left: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_column_spanner_outer:last-child {
|
||||
padding-right: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_column_spanner {
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
vertical-align: bottom;
|
||||
padding-top: 5px;
|
||||
padding-bottom: 5px;
|
||||
overflow-x: hidden;
|
||||
display: inline-block;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_spanner_row {
|
||||
border-bottom-style: hidden;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_group_heading {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
text-transform: inherit;
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
vertical-align: middle;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_empty_group_heading {
|
||||
padding: 0.5px;
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_from_md > :first-child {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_from_md > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_row {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
margin: 10px;
|
||||
border-top-style: solid;
|
||||
border-top-width: 1px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
vertical-align: middle;
|
||||
overflow-x: hidden;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_stub {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
text-transform: inherit;
|
||||
border-right-style: solid;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_stub_row_group {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
text-transform: inherit;
|
||||
border-right-style: solid;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_row_group_first td {
|
||||
border-top-width: 2px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_row_group_first th {
|
||||
border-top-width: 2px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_summary_row {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
text-transform: inherit;
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_first_summary_row {
|
||||
border-top-style: solid;
|
||||
border-top-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_first_summary_row.thick {
|
||||
border-top-width: 2px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_last_summary_row {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_grand_summary_row {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
text-transform: inherit;
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_first_grand_summary_row {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-top-style: double;
|
||||
border-top-width: 6px;
|
||||
border-top-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_last_grand_summary_row_top {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-bottom-style: double;
|
||||
border-bottom-width: 6px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_striped {
|
||||
background-color: rgba(128, 128, 128, 0.05);
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_table_body {
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_footnotes {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
border-bottom-style: none;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 2px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_footnote {
|
||||
margin: 0px;
|
||||
font-size: 90%;
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_sourcenotes {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
border-bottom-style: none;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 2px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_sourcenote {
|
||||
font-size: 90%;
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_right {
|
||||
text-align: right;
|
||||
font-variant-numeric: tabular-nums;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_font_normal {
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_font_bold {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_font_italic {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_super {
|
||||
font-size: 65%;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_footnote_marks {
|
||||
font-size: 75%;
|
||||
vertical-align: 0.4em;
|
||||
position: initial;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_asterisk {
|
||||
font-size: 100%;
|
||||
vertical-align: 0;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_indent_1 {
|
||||
text-indent: 5px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_indent_2 {
|
||||
text-indent: 10px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_indent_3 {
|
||||
text-indent: 15px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_indent_4 {
|
||||
text-indent: 20px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .gt_indent_5 {
|
||||
text-indent: 25px;
|
||||
}
|
||||
|
||||
#aphfsmpepd .katex-display {
|
||||
display: inline-flex !important;
|
||||
margin-bottom: 0.75em !important;
|
||||
}
|
||||
|
||||
#aphfsmpepd div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
|
||||
height: 0px !important;
|
||||
}
|
||||
</style>
|
||||
<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
|
||||
<thead>
|
||||
<tr class="gt_col_headings">
|
||||
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'><strong>Characteristic</strong></span></th>
|
||||
<th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_0"><span class='gt_from_md'><strong>Overall</strong><br />
|
||||
N = 416</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
|
||||
<th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_1"><span class='gt_from_md'><strong>No early vasopressor</strong><br />
|
||||
N = 181</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
|
||||
<th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_2"><span class='gt_from_md'><strong>Early vasopressor</strong><br />
|
||||
N = 235</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody class="gt_table_body">
|
||||
<tr><td headers="label" class="gt_row gt_left">age</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">66 (14)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">63 (14)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">67 (14)</td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left">sex</td>
|
||||
<td headers="stat_0" class="gt_row gt_center"><br /></td>
|
||||
<td headers="stat_1" class="gt_row gt_center"><br /></td>
|
||||
<td headers="stat_2" class="gt_row gt_center"><br /></td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left"> female</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">201 (48%)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">91 (50%)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">110 (47%)</td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left"> male</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">215 (52%)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">90 (50%)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">125 (53%)</td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left">sofa_score</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">7.00 (2.60)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">6.29 (2.50)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">7.55 (2.55)</td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left">lactate</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">3.98 (1.86)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">3.68 (1.60)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">4.22 (2.01)</td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left">map</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">55.4 (6.3)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">56.4 (6.0)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">54.6 (6.4)</td></tr>
|
||||
<tr><td headers="label" class="gt_row gt_left">death_28d</td>
|
||||
<td headers="stat_0" class="gt_row gt_center">131 (31%)</td>
|
||||
<td headers="stat_1" class="gt_row gt_center">47 (26%)</td>
|
||||
<td headers="stat_2" class="gt_row gt_center">84 (36%)</td></tr>
|
||||
</tbody>
|
||||
<tfoot>
|
||||
<tr class="gt_footnotes">
|
||||
<td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> <span class='gt_from_md'>Mean (SD); n (%)</span></td>
|
||||
</tr>
|
||||
</tfoot>
|
||||
</table>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,490 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8"/>
|
||||
<style>body{background-color:white;}</style>
|
||||
|
||||
|
||||
</head>
|
||||
<body>
|
||||
<div id="nplmwnpuqt" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
|
||||
<style>#nplmwnpuqt table {
|
||||
font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
}
|
||||
|
||||
#nplmwnpuqt thead, #nplmwnpuqt tbody, #nplmwnpuqt tfoot, #nplmwnpuqt tr, #nplmwnpuqt td, #nplmwnpuqt th {
|
||||
border-style: none;
|
||||
}
|
||||
|
||||
#nplmwnpuqt p {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_table {
|
||||
display: table;
|
||||
border-collapse: collapse;
|
||||
line-height: normal;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
color: #333333;
|
||||
font-size: 16px;
|
||||
font-weight: normal;
|
||||
font-style: normal;
|
||||
background-color: #FFFFFF;
|
||||
width: auto;
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #A8A8A8;
|
||||
border-right-style: none;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #A8A8A8;
|
||||
border-left-style: none;
|
||||
border-left-width: 2px;
|
||||
border-left-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_caption {
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_title {
|
||||
color: #333333;
|
||||
font-size: 125%;
|
||||
font-weight: initial;
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-bottom-color: #FFFFFF;
|
||||
border-bottom-width: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_subtitle {
|
||||
color: #333333;
|
||||
font-size: 85%;
|
||||
font-weight: initial;
|
||||
padding-top: 3px;
|
||||
padding-bottom: 5px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-top-color: #FFFFFF;
|
||||
border-top-width: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_heading {
|
||||
background-color: #FFFFFF;
|
||||
text-align: center;
|
||||
border-bottom-color: #FFFFFF;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_bottom_border {
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_col_headings {
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_col_heading {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: normal;
|
||||
text-transform: inherit;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
vertical-align: bottom;
|
||||
padding-top: 5px;
|
||||
padding-bottom: 6px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
overflow-x: hidden;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_column_spanner_outer {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: normal;
|
||||
text-transform: inherit;
|
||||
padding-top: 0;
|
||||
padding-bottom: 0;
|
||||
padding-left: 4px;
|
||||
padding-right: 4px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_column_spanner_outer:first-child {
|
||||
padding-left: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_column_spanner_outer:last-child {
|
||||
padding-right: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_column_spanner {
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
vertical-align: bottom;
|
||||
padding-top: 5px;
|
||||
padding-bottom: 5px;
|
||||
overflow-x: hidden;
|
||||
display: inline-block;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_spanner_row {
|
||||
border-bottom-style: hidden;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_group_heading {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
text-transform: inherit;
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
vertical-align: middle;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_empty_group_heading {
|
||||
padding: 0.5px;
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_from_md > :first-child {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_from_md > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_row {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
margin: 10px;
|
||||
border-top-style: solid;
|
||||
border-top-width: 1px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 1px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 1px;
|
||||
border-right-color: #D3D3D3;
|
||||
vertical-align: middle;
|
||||
overflow-x: hidden;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_stub {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
text-transform: inherit;
|
||||
border-right-style: solid;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_stub_row_group {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
font-size: 100%;
|
||||
font-weight: initial;
|
||||
text-transform: inherit;
|
||||
border-right-style: solid;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_row_group_first td {
|
||||
border-top-width: 2px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_row_group_first th {
|
||||
border-top-width: 2px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_summary_row {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
text-transform: inherit;
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_first_summary_row {
|
||||
border-top-style: solid;
|
||||
border-top-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_first_summary_row.thick {
|
||||
border-top-width: 2px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_last_summary_row {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_grand_summary_row {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
text-transform: inherit;
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_first_grand_summary_row {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-top-style: double;
|
||||
border-top-width: 6px;
|
||||
border-top-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_last_grand_summary_row_top {
|
||||
padding-top: 8px;
|
||||
padding-bottom: 8px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
border-bottom-style: double;
|
||||
border-bottom-width: 6px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_striped {
|
||||
background-color: rgba(128, 128, 128, 0.05);
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_table_body {
|
||||
border-top-style: solid;
|
||||
border-top-width: 2px;
|
||||
border-top-color: #D3D3D3;
|
||||
border-bottom-style: solid;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_footnotes {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
border-bottom-style: none;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 2px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_footnote {
|
||||
margin: 0px;
|
||||
font-size: 90%;
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_sourcenotes {
|
||||
color: #333333;
|
||||
background-color: #FFFFFF;
|
||||
border-bottom-style: none;
|
||||
border-bottom-width: 2px;
|
||||
border-bottom-color: #D3D3D3;
|
||||
border-left-style: none;
|
||||
border-left-width: 2px;
|
||||
border-left-color: #D3D3D3;
|
||||
border-right-style: none;
|
||||
border-right-width: 2px;
|
||||
border-right-color: #D3D3D3;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_sourcenote {
|
||||
font-size: 90%;
|
||||
padding-top: 4px;
|
||||
padding-bottom: 4px;
|
||||
padding-left: 5px;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_right {
|
||||
text-align: right;
|
||||
font-variant-numeric: tabular-nums;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_font_normal {
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_font_bold {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_font_italic {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_super {
|
||||
font-size: 65%;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_footnote_marks {
|
||||
font-size: 75%;
|
||||
vertical-align: 0.4em;
|
||||
position: initial;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_asterisk {
|
||||
font-size: 100%;
|
||||
vertical-align: 0;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_indent_1 {
|
||||
text-indent: 5px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_indent_2 {
|
||||
text-indent: 10px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_indent_3 {
|
||||
text-indent: 15px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_indent_4 {
|
||||
text-indent: 20px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .gt_indent_5 {
|
||||
text-indent: 25px;
|
||||
}
|
||||
|
||||
#nplmwnpuqt .katex-display {
|
||||
display: inline-flex !important;
|
||||
margin-bottom: 0.75em !important;
|
||||
}
|
||||
|
||||
#nplmwnpuqt div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
|
||||
height: 0px !important;
|
||||
}
|
||||
</style>
|
||||
<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
|
||||
<thead>
|
||||
<tr class="gt_heading">
|
||||
<td colspan="2" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Naive 28-Day Mortality Comparison</td>
|
||||
</tr>
|
||||
|
||||
<tr class="gt_col_headings">
|
||||
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="measure">Measure</th>
|
||||
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="value">Value</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody class="gt_table_body">
|
||||
<tr><td headers="measure" class="gt_row gt_left">n_eligible</td>
|
||||
<td headers="value" class="gt_row gt_right">416.000</td></tr>
|
||||
<tr><td headers="measure" class="gt_row gt_left">n_early</td>
|
||||
<td headers="value" class="gt_row gt_right">235.000</td></tr>
|
||||
<tr><td headers="measure" class="gt_row gt_left">n_not_early</td>
|
||||
<td headers="value" class="gt_row gt_right">181.000</td></tr>
|
||||
<tr><td headers="measure" class="gt_row gt_left">risk_early</td>
|
||||
<td headers="value" class="gt_row gt_right">0.357</td></tr>
|
||||
<tr><td headers="measure" class="gt_row gt_left">risk_not_early</td>
|
||||
<td headers="value" class="gt_row gt_right">0.260</td></tr>
|
||||
<tr><td headers="measure" class="gt_row gt_left">risk_difference</td>
|
||||
<td headers="value" class="gt_row gt_right">0.098</td></tr>
|
||||
<tr><td headers="measure" class="gt_row gt_left">risk_ratio</td>
|
||||
<td headers="value" class="gt_row gt_right">1.377</td></tr>
|
||||
</tbody>
|
||||
|
||||
</table>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
Executable
+22
@@ -0,0 +1,22 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# Run the current learning project workflow end to end.
|
||||
#
|
||||
# This is intentionally simpler than a targets pipeline.
|
||||
# It gives us one command that runs the simulation smoke check and renders the
|
||||
# Quarto notebooks while the project is still small.
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
# Make the script work even if it is called from another directory.
|
||||
script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
cd "$script_dir"
|
||||
|
||||
mkdir -p "outputs/reports"
|
||||
|
||||
Rscript "scripts/01_simulate_icu_data.R"
|
||||
|
||||
quarto render "notebooks/01_target_trial_basics.qmd" --output-dir "../outputs/reports"
|
||||
quarto render "notebooks/02_explore_simulated_data.qmd" --output-dir "../outputs/reports"
|
||||
|
||||
printf '\nWorkflow complete. Reports are in outputs/reports/.\n'
|
||||
@@ -0,0 +1,24 @@
|
||||
# Simulate an ICU cohort for learning target trial emulation.
|
||||
# The simulation logic lives in R/simulate_icu_cohort.R.
|
||||
|
||||
# suppressPackageStartupMessages() keeps package startup text out of the console.
|
||||
# This makes the important output easier to read when learning.
|
||||
suppressPackageStartupMessages({
|
||||
library(skimr)
|
||||
})
|
||||
|
||||
# source() runs another R file.
|
||||
# After this line runs, simulate_icu_cohort() is available in this script.
|
||||
source("R/simulate_icu_cohort.R")
|
||||
|
||||
# This calls our reusable simulation function.
|
||||
# n_patients controls the number of rows.
|
||||
# seed controls reproducibility.
|
||||
icu_data <- simulate_icu_cohort(n_patients = 1000, seed = 20260531)
|
||||
|
||||
# cli::cli_alert_success() prints a nicely formatted success message.
|
||||
cli::cli_alert_success("Simulated an in-memory ICU cohort with {nrow(icu_data)} patients.")
|
||||
|
||||
# skim() gives a quick learner-friendly overview of the dataset.
|
||||
# It shows variable types, missingness, summary statistics, and small histograms.
|
||||
skim(icu_data)
|
||||
@@ -1,18 +0,0 @@
|
||||
# Simulate an ICU cohort for learning target trial emulation.
|
||||
# The simulation logic lives in R/simulate_icu_cohort.R.
|
||||
|
||||
suppressPackageStartupMessages(library(readr))
|
||||
suppressPackageStartupMessages(library(skimr))
|
||||
|
||||
source("R/simulate_icu_cohort.R")
|
||||
|
||||
icu_data <- simulate_icu_cohort(n_patients = 1000, seed = 20260531)
|
||||
|
||||
write_csv(
|
||||
icu_data,
|
||||
file = "data/icu_septic_shock_base_r.csv"
|
||||
)
|
||||
|
||||
cli::cli_alert_success("Wrote simulated cohort to {.file data/icu_septic_shock_base_r.csv}.")
|
||||
|
||||
skim(icu_data)
|
||||
@@ -1,91 +0,0 @@
|
||||
suppressPackageStartupMessages(library(dplyr))
|
||||
suppressPackageStartupMessages(library(gt))
|
||||
suppressPackageStartupMessages(library(gtsummary))
|
||||
suppressPackageStartupMessages(library(readr))
|
||||
suppressPackageStartupMessages(library(tidyr))
|
||||
|
||||
cli::cli_h1("Naive analysis of the simulated ICU cohort")
|
||||
|
||||
icu_data <- read_csv("data/icu_septic_shock_base_r.csv", show_col_types = FALSE)
|
||||
|
||||
if (!dir.exists("outputs")) {
|
||||
dir.create("outputs")
|
||||
}
|
||||
|
||||
eligible_data <- icu_data |>
|
||||
filter(eligible == 1)
|
||||
|
||||
analysis_summary <- eligible_data |>
|
||||
summarize(
|
||||
n_eligible = n(),
|
||||
n_early = sum(early_vasopressor == 1),
|
||||
n_not_early = sum(early_vasopressor == 0),
|
||||
risk_early = mean(death_28d[early_vasopressor == 1]),
|
||||
risk_not_early = mean(death_28d[early_vasopressor == 0]),
|
||||
risk_difference = risk_early - risk_not_early,
|
||||
risk_ratio = risk_early / risk_not_early
|
||||
)
|
||||
|
||||
analysis_results <- analysis_summary |>
|
||||
pivot_longer(
|
||||
cols = everything(),
|
||||
names_to = "measure",
|
||||
values_to = "value"
|
||||
)
|
||||
|
||||
analysis_table <- analysis_results |>
|
||||
gt() |>
|
||||
tab_header(title = "Naive 28-Day Mortality Comparison") |>
|
||||
cols_label(
|
||||
measure = "Measure",
|
||||
value = "Value"
|
||||
) |>
|
||||
fmt_number(columns = value, decimals = 3)
|
||||
|
||||
baseline_table <- eligible_data |>
|
||||
mutate(
|
||||
early_vasopressor = factor(
|
||||
early_vasopressor,
|
||||
levels = c(0, 1),
|
||||
labels = c("No early vasopressor", "Early vasopressor")
|
||||
)
|
||||
) |>
|
||||
select(early_vasopressor, age, sex, sofa_score, lactate, map, death_28d) |>
|
||||
tbl_summary(
|
||||
by = early_vasopressor,
|
||||
statistic = list(
|
||||
all_continuous() ~ "{mean} ({sd})",
|
||||
all_categorical() ~ "{n} ({p}%)"
|
||||
),
|
||||
missing = "no"
|
||||
) |>
|
||||
add_overall()
|
||||
|
||||
baseline_console <- eligible_data |>
|
||||
group_by(early_vasopressor) |>
|
||||
summarize(
|
||||
mean_age = mean(age),
|
||||
mean_sofa_score = mean(sofa_score),
|
||||
mean_lactate = mean(lactate),
|
||||
mean_map = mean(map),
|
||||
.groups = "drop"
|
||||
)
|
||||
|
||||
cli::cli_h2("Core causal contrast")
|
||||
print(analysis_results)
|
||||
|
||||
gtsave(
|
||||
data = analysis_table,
|
||||
filename = "outputs/naive_mortality_comparison.html"
|
||||
)
|
||||
|
||||
cli::cli_alert_success("Wrote {.file outputs/naive_mortality_comparison.html}.")
|
||||
|
||||
cli::cli_h2("Baseline comparison by observed treatment")
|
||||
print(baseline_console)
|
||||
|
||||
baseline_table |>
|
||||
as_gt() |>
|
||||
gtsave(filename = "outputs/baseline_by_treatment.html")
|
||||
|
||||
cli::cli_alert_success("Wrote {.file outputs/baseline_by_treatment.html}.")
|
||||
Reference in New Issue
Block a user