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---
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title: "Treatment Assignment Windows and Cloning"
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format:
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html:
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embed-resources: true
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docx: default
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execute:
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echo: true
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warning: false
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message: false
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---
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## Goal
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This notebook introduces two core target trial emulation mechanics:
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1. **Treatment assignment windows** (the grace period): the time during which a treatment decision is made.
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2. **Cloning**: at time zero, each eligible patient is duplicated into every treatment arm under study.
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These ideas move us from a cross-sectional baseline comparison to a longitudinal design that more closely emulates a randomized trial.
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## Setup
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```{r}
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suppressPackageStartupMessages({
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library(dplyr)
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library(gt)
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library(tibble)
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library(tidyr)
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})
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source("../R/simulate_icu_cohort.R")
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source("../R/simulate_icu_cohort_longitudinal.R")
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source("../R/clone_trial_arms.R")
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```
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## Simulate Longitudinal Data
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We now generate data with multiple rows per patient: one row for each time point at which we could observe a measurement or event.
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Time points:
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- `0h` — ICU admission (baseline)
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- `2h` — end of the treatment decision window
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- `6h, 12h, 24h` — early ICU course
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- `day7, day14, day21, day28` — outcome assessment
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```{r}
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longitudinal_data <- simulate_icu_cohort_longitudinal(n_patients = 1000, seed = 20260531)
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longitudinal_data |>
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filter(patient_id == 1)
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```
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Each row shows the patient's state at one time point.
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Notice:
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- `vasopressor_started` is `0` before the treatment time and `1` after.
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- `map_current` and `lactate_current` evolve slightly once vasopressors start.
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- `alive` becomes `0` after death (if the patient died before day 28).
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## Three Example Patient Trajectories
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To see the structure more clearly, here are three patients side by side.
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```{r}
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example_patients <- longitudinal_data |>
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filter(patient_id %in% c(1, 5, 10))
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example_patients |>
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select(
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patient_id, time_label, vasopressor_started,
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map, map_current, lactate, lactate_current,
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alive, death_28d
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) |>
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gt() |>
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tab_header(title = "Example Patient Trajectories") |>
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cols_label(
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patient_id = "Patient ID",
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time_label = "Time",
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vasopressor_started = "Vasopressors started",
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map = "Baseline MAP",
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map_current = "Current MAP",
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lactate = "Baseline lactate",
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lactate_current = "Current lactate",
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alive = "Alive",
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death_28d = "Death by day 28"
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)
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```
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These trajectories show that treatment is not assigned instantaneously at time zero.
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Instead, there is a **grace period** (0 to 2 hours) during which clinicians decide whether to start vasopressors.
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## What Is a Grace Period?
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In the target trial we want to emulate:
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- **Strategy A**: start vasopressors within 2 hours of ICU admission.
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- **Strategy B**: do not start vasopressors within 2 hours of ICU admission.
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The **2-hour window** is the treatment assignment window, also called the grace period.
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During this window, patients may receive monitoring, fluid resuscitation, and other care. The treatment decision is made based on the patient's response.
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In the observational data, some patients start vasopressors at hour 0, some at hour 1.5, and some never start them within 2 hours.
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The grace period is what makes this a longitudinal problem rather than a simple baseline cross-section.
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## Clone the Eligible Patients
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To emulate a randomized trial, we duplicate each eligible patient at time zero into two **clones**:
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- **Clone "early"**: this patient is assigned to the early vasopressor strategy.
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- **Clone "no_early"**: this patient is assigned to the no early vasopressor strategy.
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Both clones then follow the patient's actual observed trajectory over time.
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```{r}
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clones <- clone_trial_arms(longitudinal_data)
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clones$clone_baseline |>
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count(clone_strategy)
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```
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Every eligible patient now has two rows in `clone_baseline`, one for each strategy.
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## Censoring Rules
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A clone is **censored** if the patient deviated from the assigned strategy or died before the treatment window closed.
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### Rule 1: Protocol deviation
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- The "early" clone is censored if the patient did NOT actually start vasopressors within 2 hours.
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- The "no_early" clone is censored if the patient DID actually start vasopressors within 2 hours.
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### Rule 2: Death before treatment
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- Both clones are censored if the patient died before the 2-hour window closed.
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```{r}
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clones$clone_baseline |>
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count(clone_strategy, clone_censored, clone_censored_reason) |>
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gt() |>
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tab_header(title = "Censoring Summary by Clone Strategy") |>
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cols_label(
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clone_strategy = "Clone strategy",
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clone_censored = "Censored",
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clone_censored_reason = "Censoring reason",
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n = "Count"
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)
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```
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This table shows how many clones are censored and why.
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Protocol deviation is the most common reason because the observational data does not perfectly align with either strategy.
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## One Patient, Two Clones
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To make the cloning concrete, here is patient 1 at time zero, before any censoring is applied.
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```{r}
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clones$clone_baseline |>
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filter(patient_id == 1) |>
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select(
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patient_id, clone_strategy, age, sex, sofa_score,
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lactate, map, early_vasopressor, clone_censored, clone_censored_reason
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) |>
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gt() |>
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tab_header(title = "Patient 1: Two Clones at Time Zero") |>
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cols_label(
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patient_id = "Patient ID",
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clone_strategy = "Clone strategy",
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age = "Age",
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sex = "Sex",
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sofa_score = "SOFA",
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lactate = "Lactate",
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map = "MAP",
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early_vasopressor = "Observed early treatment",
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clone_censored = "Clone censored?",
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clone_censored_reason = "Reason"
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)
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```
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Both clones share the same baseline characteristics because they come from the same patient.
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They differ only in the strategy they are assigned to.
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## What We Have So Far
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At this point in the target trial emulation:
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1. We identified eligible patients at ICU admission (time zero).
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2. We cloned each eligible patient into two treatment arms.
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3. We applied censoring rules at the 2-hour mark.
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4. Uncensored clones continue to follow the patient's trajectory.
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5. Censored clones drop out and do not contribute to later time points.
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The next step is to account for the fact that censoring is not random.
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Some patients are more likely to be censored because of their baseline severity. We handle that with **inverse probability of censoring weighting (IPCW)** in the next notebook.
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## Next Step
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The next tutorial step is to compute inverse probability of treatment and censoring weights on the cloned dataset, and estimate a per-protocol effect that accounts for both baseline confounding and informative censoring.
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