
---
title: "Package Introduction"
package: RobinCar2
output:
  rmarkdown::html_vignette:
          toc: true
vignette: |
  %\VignetteIndexEntry{Package Introduction}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
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---

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

RobinCar2 provides robust covariate adjustment methods for estimating and
inferring treatment effects through generalized linear models (glm) under
different randomization schema.

# Common Usage
A minimal call of `robin_lm()` and `robin_glm()`, consisting of only formula,
data arguments and the randomization scheme, will produce an object of class
`treatment_effect`.

```{r}
library(RobinCar2)
head(dummy_data)
```

## Data Introduction

In the `dummy_data`, we have the following columns:

1. `id` is the patient identifier.
1. `treatment` is the treatment assignment.
1. `s1` is the first stratification factor.
1. `s2` is the second stratification factor.
1. `covar` is the continuous covariate.
1. `y` is the continuous outcome.
1. `y_b` is the binary outcome.

## Obtain Treatment Effect for Continuous Outcomes Using the General Variance

For the continuous outcome `y`, the linear model includes `covar` as a covariate,
`s1` as a stratification factor. The randomization scheme is a permuted-block
randomization stratified by `s1`. The model formula also includes the treatment
by stratification interaction as `y ~ treatment * s1 + covar`.

```{r}
robin_lm(y ~ treatment * s1 + covar,
  data = dummy_data,
  treatment = treatment ~ pb(s1)
)
```

We can also use the Huber-White variance estimator by setting `vcov = "vcovHC"`.
Please note that in this case, the model formula should not contain the
treatment by stratification (covariate) interaction.

```{r}
robin_lm(y ~ treatment + s1 + covar,
  data = dummy_data,
  treatment = treatment ~ pb(s1),
  vcov = "vcovHC"
)
```

Note that `robin_glm` can also handle continuous outcomes using the default
family and the default link function `family = gaussian()`.

## Obtain Treatment Effect for Binary Outcomes

For binary outcomes, the logistic model includes `covar` as a covariate,
`s1` as a stratification factor. The randomization scheme is a permuted-block
randomization stratified by `s1`. The model formula also includes the treatment
by stratification interaction as `y_b ~ treatment * s1 + covar`. Note here we need
to specify `family` to be `binomial(link = "logit")`.

```{r}
robin_glm(y_b ~ treatment * s1 + covar,
  data = dummy_data,
  treatment = treatment ~ pb(s1),
  family = binomial(link = "logit")
)
```

## Obtain Treatment Effect for Counts

For counts, the log link model includes `covar` as a covariate,
`s1` as a stratification factor. The randomization scheme is a permuted-block
randomization stratified by `s1`. The model formula also includes the treatment
by stratification interaction as `y_count ~ treatment * s1 + covar`. Note here
we need to specify `family` to be `poisson(link = "log")` to use the Poisson
model or to be `MASS::negative.binomial(theta = NA)` to use the negative
binomial model. A fixed `theta` could be provided if it is known.

```{r}
dummy_data$y_count <- rpois(nrow(dummy_data), lambda = 20)
robin_glm(
  y_count ~ treatment * s1 + covar,
  data = dummy_data,
  treatment = treatment ~ pb(s1),
  family = MASS::negative.binomial(theta = 1)
)
```

## Using Different Covariate-Adaptive Randomization Schema

If the randomization schema is not permuted-block randomization, we can use
other randomization schema. Currently RobinCar2 supports `sp` for the simple
randomization, `pb` for the permuted-block randomization, and `ps` for the
Pocock-Simon randomization.

```{r}
robin_glm(y_b ~ treatment * s1 + covar,
  data = dummy_data,
  treatment = treatment ~ ps(s1),
  family = binomial(link = "logit")
)
```
