In shuyang1987/psace: Multiply Robust Estimators of Average Causal Effects within principal strata (PSACE)

```r knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )

# psace

The goal of *psace* is to 
implement various estimators of Average Causal Effects within principal strata (PS-ACEs) from randomized experiments and observational studies.


## Installation with `devtools`:

```r
devtools::install_github("shuyang1987/psace")

Main Paper: coming soon

The reference paper will come soon.

Usage

psace(X,Z,S,Y,family.Y,nboot)

Arguments

Argument | ------------- | ------------- X| is a matrix of pre-treatment covariates without intercept (n x p). Z| is a vector of treatment (n x 1). S| is a vector of binary intermediate outcome (n x 1). Y| is a vector of outcome ( n x 1). family.Y| specifies the family for the outcome model. |"gaussian": a linear regression model for the continuous outcome. |"binomial": a logistic regression model for the binary outcome. nboot| is the number of bootstrap samples.

Value

| ------------- | ------------- Complier| tau10w| a principal score weighting estimator of tau10 tau10sw| a stabilized weighting estimator of tau10 tau10reg| a regression estimator of tau10 using outcome mean regression and inverse probability of treatment weighting tau10reg2| a regression estimator of tau10 using outcome mean and principal score regression tau10aw| a triply robust estimator of tau10 bootstrap variance estimator|ve.tau10w,ve.tau10sw,ve.tau10reg,ve.tau10reg2,ve.tau10aw

| ------------- | ------------- Never Taker| tau00w| a principal score weighting estimator of tau00 tau00sw| a stabilized weighting estimator of tau00 tau00reg| a regression estimator of tau00 using outcome mean regression and inverse probability of treatment weighting tau00reg2| a regression estimator of tau00 using outcome mean and principal score regression tau00aw| a triply robust estimator of tau00 bootstrap variance estimator|ve.tau00w,ve.tau00sw,ve.tau00reg,ve.tau00reg2,ve.tau00aw

| ------------- | ------------- Always Taker| tau11w| a principal score weighting estimator of tau11 tau11sw| a stabilized weighting estimator of tau11 tau11reg| a regression estimator of tau11 using outcome mean regression and inverse probability of treatment weighting tau11reg2| a regression estimator of tau11 using outcome mean and principal score regression tau11aw| a triply robust estimator of tau11 bootstrap variance estimator|ve.tau11w,ve.tau11sw,ve.tau11reg,ve.tau11reg2,ve.tau11aw

Example

This is an example for illustration.

library(stats)

set.seed(1)
#X consists of 5 covariates
n <- 1000
X <- rnorm(n,0.25,1)
for(k in 2:4){
  X <- cbind(X,rnorm(n,0.25,1))
}
X <- cbind(X,rbinom(n,1,0.5))

# treatment assignment
Xtilde1 <- (X-0.25)/1
theta <- 0
eta <- c(0,1,1,1,1,theta)/2.5
px0 <- exp(cbind(1,Xtilde1)%*%eta)
pix  <- px0/(1+px0)
Z   <- rbinom(n,1,pix)

# S-model
eta1 <- c( 2,-1,1,-1,1,theta)/2.5
eta0 <- c(-2,1,-1,1,-1,theta)/2.5
p1xtemp <- exp(cbind(1,Xtilde1)%*%eta1)
p0xtemp <- exp(cbind(1,Xtilde1)%*%eta0)
p1x <- p1xtemp/(1+p1xtemp)
p0x <- p0xtemp/(1+p0xtemp)
s1<-rbinom(n,1,p1x)
s0<-rbinom(n,1,p0x)
S<-s1*Z+s0*(1-Z)

# Y-model (continuous outcome)
Y1<- Xtilde1%*%rep(1,5)*(1+S+Z) +rnorm(n)
# Y-model (binary outcome)
leYtemp <- Xtilde1%*%rep(1,5)*(1+S+Z)/4
eY<-exp(leYtemp)/(1+exp(leYtemp) )
Y2<-rbinom(n,1,eY)

out1<-PSACE::psace(X,Z,S,Y1,family.Y="gaussain",nboot=100)
out1$tau10aw
out1$ve.tau10aw

out2<-PSACE::psace(X,Z,S,Y2,family.Y="binomial",nboot=100)
out2$tau10aw
out2$ve.tau10aw

shuyang1987/psace documentation built on May 6, 2020, 12:37 a.m.