README.Rmd

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output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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)
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# complyr

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The goal of complyr is to create a one-stop package for analyzing compiler 
causal effects. 

Unignorable confounding is no stranger even for randomized 
controlled trials (RCTs) in the presence of treatment non-compliance. 
One fallback is the intention-to-treat (ITT) analysis, which unfortunately 
only reflects the assignment-induced causal effect. The celebrated 
(Angrist, Imbens & Rubin 1996) framework provides an excellent platform 
to tackle unmeasured confounding for estimating the complier (local) average 
treatment effect (ATE). This package aims to provide a suite of tools for 
analyzing different complier causal effects including ATE, quantile treatment
effect (QTE), and probabilistic index.  

## Installation

You can install the development version of complyr from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("ge-li/complyr")
```

## Example

This is a basic example which shows you how to estimate a complier probabilistic index model,
with comparison to intention-to-treat and per-protocol methods. 

```{r example}
library(complyr)
# Simulate some RCT data with non-compliance, see details in function docs. 
set.seed(42)
df <- generate_data(
  n_obs = 2000,
  ps_type = "rct",
  p_c = 0.8,
  alpha = 1,
  beta_1 = 0.5,
  beta_2 = -0.7,
  error_dist = "gumbel"
)
# Estimate the complier probabilistic index models
# We use three methods to analyze this data set: ITT, PP, IV
# ITT: intention-to-treat
itt_fit <- upim::pim_fit(y = df$y, X = df[, c("z", "x1", "x2")], link = "logit")
# PP: per-protocol
df_pp <- df[df$z == df$a, ]
pp_fit <- upim::pim_fit(y = df_pp$y, X = df_pp[, c("a", "x1", "x2")], link = "logit")
# IV: instrumental variable
ps_model <- glm(df$z ~ 1, family = binomial(link = "logit"), x = TRUE)
iv_fit <- complyr::ivpim(y = df$y, z = df$z, a = df$a, X = df[, c("x1", "x2")], 
                         ps_model =  ps_model, link = "logit")
sum_stat <- function(model_fit) {
  # get summary stats for downstream analysis
  ss <- c(model_fit$coef, sqrt(diag(model_fit$vcov)))
  names(ss) <- c("alpha_hat", "beta_1_hat", "beta_2_hat",
                 "alpha_se", "beta_1_se", "beta_2_se")
  round(ss, 3)
}
results <- as.data.frame(rbind(sum_stat(itt_fit),
                               sum_stat(pp_fit),
                               sum_stat(iv_fit)))
results$methods <- c("itt", "pp", "iv")
results
```