---
title: "Explore Simulated ICU Data"
format:
  html:
    embed-resources: true
  docx: default
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.