---
title: "Cookbook: Incidence (Binary) Outcome, End to End"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Cookbook: Incidence (Binary) Outcome, End to End}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

One complete, runnable script for a binary ("incidence") outcome — did the
event happen or not. Same four steps as every cookbook (design → assign →
record → infer); see `vignette("cookbook-continuous")` for the narrated
version of the pattern. Here the response is 0/1, the natural estimand is
a log odds ratio (or a risk difference / risk ratio via the g-computation
classes), and the inference classes are the `InferenceIncid*` family.

## Setup

EDI is not on CRAN yet, so `install.packages("EDI")` fails — install from
R-universe (fallback: GitHub, `subdir = "R/EDI"`). Not evaluated here.

```{r install, eval = FALSE, purl = FALSE}
install.packages("EDI", repos = c("https://kapelner.r-universe.dev", "https://cloud.r-project.org"))
# or: remotes::install_github("kapelner/EDI", subdir = "R/EDI")
```

```{r setup}
library(EDI)
set.seed(20260916)

n = 80
X = data.frame(
  age    = round(rnorm(n, 50, 10)),
  smoker = rbinom(n, 1, 0.3)
)
true_log_or = 0.9
```

## Fixed design, logistic regression

```{r fixed}
des = DesignFixedBernoulli$new(n = n, response_type = "incidence", verbose = FALSE)
des$add_all_subjects_to_experiment(X)
des$assign_w_to_all_subjects()
w = des$get_w()

p = plogis(-1.2 + true_log_or * w + 0.03 * (X$age - 50) + 0.6 * X$smoker)
y = rbinom(n, 1, p)
des$add_all_subject_responses(y)

inf = InferenceIncidLogRegr$new(des, verbose = FALSE)
inf$num_cores = 1L
inf$compute_estimate()                         # log odds ratio for treatment
inf$compute_asymp_confidence_interval(alpha = 0.05)
inf$compute_asymp_two_sided_pval()
```

Randomization test and bootstrap, as in the continuous cookbook:

```{r fixed-resampling}
inf$set_seed(1)
inf$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)
inf$set_seed(1)
inf$compute_bootstrap_confidence_interval(alpha = 0.05, B = 200, show_progress = FALSE)
```

## A risk difference instead of an odds ratio

The g-computation classes estimate a marginal risk difference or risk
ratio by standardizing over the covariates — often the estimand a trial
actually reports. Same design object, different class:

```{r gcomp}
inf_rd = InferenceIncidGCompRiskDiff$new(des, verbose = FALSE)
inf_rd$num_cores = 1L
inf_rd$compute_estimate()
inf_rd$compute_asymp_confidence_interval(alpha = 0.05)
```

## Everything at once

```{r suite}
suite = InferenceSuite$new(des)
res = suite$run_all_inference(screen = TRUE, plots = FALSE, num_cores = 1L,
                              methods = c("wald", "score", "lik_ratio"), max_secs_per_class = 15)
```

## Sequential design: matching on the fly

`DesignSeqOneByOneKK14` matches each arrival to an earlier unmatched
subject when a close enough match exists. Its matched inference class for
a binary outcome, `InferenceIncidKKGCompRiskDiff`, uses the pair/reservoir
structure directly. As on every sequential design whose assignments
depend on earlier subjects, the nonparametric bootstrap is not offered;
randomization inference replays the design's own mechanism instead.

```{r seq}
des_seq = DesignSeqOneByOneKK14$new(n = n, response_type = "incidence", verbose = FALSE)
for (i in seq_len(n)) {
  w_i = des_seq$add_one_subject_to_experiment_and_assign(X[i, , drop = FALSE])
  p_i = plogis(-1.2 + true_log_or * w_i + 0.03 * (X$age[i] - 50) + 0.6 * X$smoker[i])
  des_seq$add_one_subject_response(i, rbinom(1, 1, p_i))
}

inf_seq = InferenceIncidKKGCompRiskDiff$new(des_seq, verbose = FALSE)
inf_seq$num_cores = 1L
inf_seq$compute_estimate()
inf_seq$compute_asymp_confidence_interval(alpha = 0.05)
inf_seq$set_seed(1)
inf_seq$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)
```

## Where to go next

- Every incidence class: the reference index, section *Inference:
  Incidence (Binary) Outcomes*.
- Exact (Fisher-style) and CMH procedures for stratified/blocked designs
  are in the same family — `InferenceSuite` will run whichever apply to
  your design.
- `vignette("validation-evidence")` lists how each was checked.