---
title: "Outcome Models"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{outcome_models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

After the pseudo population dataset was generated, we apply outcome models on the pseudo population as-if the dataset is from a randomized experiment.

We propose three types of outcome models using parametric, semi-parametric and non-parametric approaches, respectively.

**`estimate_pmetric_erf`** estimates the hazard ratios using a parametric regression model. By default, call **`gnm`** library to implement generalized nonlinear models.

**`estimate_semipmetric_erf`** estimates the smoothed exposure-response function using a generalized additive model with splines. By default, call **`gam`** library to implement generalized additive models.

**`estimate_npmetric_erf`** estimates the smoothed exposure-response function using a kernel smoothing approach. By default, call **`KernSmooth`** library to implement local polynomial fitting with a kernel weight. We use a data-driven bandwidth selection.