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+---
+title: "Full Emulation Pipeline"
+format:
+  html:
+    embed-resources: true
+  docx: default
+execute:
+  echo: true
+  warning: false
+  message: false
+---
+
+## Goal
+
+This notebook puts the entire target trial emulation pipeline together in one place:
+
+1. Simulate the observational data.
+2. Define eligibility at time zero.
+3. Clone eligible patients into treatment arms.
+4. Apply censoring rules at the 2-hour mark.
+5. Compute IPTW + IPCW weights.
+6. Estimate the per-protocol effect.
+
+Then we compare all four methods we have learned so far.
+
+## Setup
+
+```{r}
+suppressPackageStartupMessages({
+  library(dplyr)
+  library(gt)
+  library(tibble)
+  library(tidyr)
+})
+
+source("../R/simulate_icu_cohort.R")
+source("../R/simulate_icu_cohort_longitudinal.R")
+source("../R/estimate_naive_vasopressor_mortality_effect.R")
+source("../R/estimate_standardized_vasopressor_mortality_effect.R")
+source("../R/estimate_iptw_vasopressor_mortality_effect.R")
+source("../R/clone_trial_arms.R")
+source("../R/estimate_iptw_and_ipcw_effect.R")
+```
+
+## Step 1: Simulate Data
+
+We generate both the cross-sectional and longitudinal datasets using the same seed.
+
+```{r}
+icu_data <- simulate_icu_cohort(n_patients = 1000, seed = 20260531)
+longitudinal_data <- simulate_icu_cohort_longitudinal(n_patients = 1000, seed = 20260531)
+```
+
+## Step 2: Cross-Sectional Estimates
+
+### Naive comparison
+
+```{r}
+naive_analysis <- estimate_naive_vasopressor_mortality_effect(icu_data)
+```
+
+### Outcome regression standardization
+
+```{r}
+standardized_analysis <- estimate_standardized_vasopressor_mortality_effect(icu_data)
+```
+
+### Cross-sectional IPTW
+
+```{r}
+iptw_analysis <- estimate_iptw_vasopressor_mortality_effect(icu_data)
+```
+
+## Step 3: Longitudinal Emulation
+
+### Clone and censor
+
+```{r}
+clones <- clone_trial_arms(longitudinal_data)
+```
+
+### Compute IPTW + IPCW weights and estimate effect
+
+```{r}
+longitudinal_analysis <- estimate_iptw_and_ipcw_effect(clones$clone_baseline)
+```
+
+## Four-Way Comparison
+
+We now extract the risk difference and risk ratio from each method and place them in one table.
+
+```{r}
+# Naive
+naive_rd <- naive_analysis$mortality_effect_estimates |>
+  filter(estimate == "Naive risk difference") |>
+  pull(value)
+
+naive_rr <- naive_analysis$mortality_effect_estimates |>
+  filter(estimate == "Naive risk ratio") |>
+  pull(value)
+
+# Standardized
+std_rd <- standardized_analysis$standardized_mortality_effect_estimates |>
+  filter(estimate == "Standardized risk difference") |>
+  pull(value)
+
+std_rr <- standardized_analysis$standardized_mortality_effect_estimates |>
+  filter(estimate == "Standardized risk ratio") |>
+  pull(value)
+
+# Cross-sectional IPTW
+iptw_rd <- iptw_analysis$iptw_mortality_effect_estimates |>
+  filter(estimate == "IPTW risk difference") |>
+  pull(value)
+
+iptw_rr <- iptw_analysis$iptw_mortality_effect_estimates |>
+  filter(estimate == "IPTW risk ratio") |>
+  pull(value)
+
+# Longitudinal IPTW + IPCW
+long_rd <- longitudinal_analysis$weighted_effect_estimates |>
+  filter(estimate == "IPTW + IPCW risk difference") |>
+  pull(value)
+
+long_rr <- longitudinal_analysis$weighted_effect_estimates |>
+  filter(estimate == "IPTW + IPCW risk ratio") |>
+  pull(value)
+
+four_way <- tibble(
+  method = c(
+    "1. Naive observed comparison",
+    "2. Outcome regression standardization",
+    "3. Cross-sectional IPTW",
+    "4. Longitudinal IPTW + IPCW (per-protocol)"
+  ),
+  risk_difference = c(naive_rd, std_rd, iptw_rd, long_rd),
+  risk_ratio = c(naive_rr, std_rr, iptw_rr, long_rr)
+)
+
+four_way |>
+  gt() |>
+  tab_header(title = "Four-Way Comparison of Effect Estimation Methods") |>
+  cols_label(
+    method = "Method",
+    risk_difference = "Risk difference",
+    risk_ratio = "Risk ratio"
+  ) |>
+  fmt_number(columns = c(risk_difference, risk_ratio), decimals = 3)
+```
+
+## Interpretation
+
+### What each method answers
+
+| Method | Question it answers |
+|--------|-------------------|
+| **Naive** | "What was the mortality difference between patients who did and did not receive early vasopressors?" |
+| **Standardization** | "What would the mortality risk be if everyone followed each strategy, holding the eligible population fixed?" |
+| **Cross-sectional IPTW** | "What is the effect of receiving early vasopressors versus not, reweighted to balance confounders?" |
+| **Longitudinal IPTW + IPCW** | "What is the per-protocol effect of the early vasopressor strategy versus the no early strategy, respecting the grace period and accounting for censoring?" |
+
+### What to notice in the simulated data
+
+In the data-generating process, early vasopressors have a modest **protective** effect.
+
+1. The **naive** estimate shows **harm** (positive risk difference, risk ratio > 1). This is because sicker patients are more likely to receive early vasopressors and more likely to die — confounding by indication.
+
+2. **Standardization** and **cross-sectional IPTW** both move the estimate toward a **protective** direction, correcting for baseline confounding.
+
+3. The **longitudinal IPTW + IPCW** estimate may differ slightly from cross-sectional IPTW because it:
+   - Restricts to the per-protocol population (patients who could adhere through the 2-hour window)
+   - Re-weights for informative censoring (patients censored due to protocol deviation or early death)
+   - Respects the grace period as a longitudinal treatment assignment window
+
+None of the methods perfectly recover the true effect in a single finite sample, but the longitudinal method most closely emulates the target trial design.
+
+## Assumption Checklist
+
+Every method above relies on assumptions. A quick checklist:
+
+| Assumption | Naive | Standardized | IPTW | IPTW + IPCW |
+|------------|-------|-------------|------|-------------|
+| No unmeasured confounding | ❌ | ✅ | ✅ | ✅ |
+| Positivity (every patient has some probability of each treatment) | ❌ | ✅ | ✅ | ✅ |
+| Correct model specification | N/A | ✅ | ✅ | ✅ |
+| No informative censoring | N/A | N/A | N/A | ✅ |
+
+- **No unmeasured confounding**: We must have measured all variables that affect both treatment assignment and mortality. In a real study, this is never fully testable.
+- **Positivity**: Every patient must have a non-zero probability of receiving each treatment. Extreme propensity scores cause unstable weights.
+- **Correct model specification**: The logistic regression models must capture the true relationships between confounders, treatment, and outcome.
+- **No informative censoring (for IPCW)**: Censoring must be independent of the outcome given measured confounders. In practice, we use baseline-only IPCW here; a full analysis would include time-varying covariates.
+
+## Next Steps
+
+From here, the tutorial can extend into several directions:
+
+1. **Time-varying confounders**: Include `map_current` and `lactate_current` in the censoring model to handle post-baseline severity changes.
+2. **Survival analysis**: Model time-to-death rather than 28-day binary mortality, using Kaplan-Meier or Cox models with IPCW.
+3. **Sensitivity analysis**: Test how sensitive the results are to unmeasured confounding (e.g., E-values).
+4. **`targets` pipeline**: Convert the analysis into a reproducible pipeline with dependency tracking.
+
+These topics move the project from a teaching scaffold toward a production-ready target trial emulation workflow.