binregATE: Average Treatment effect for censored competing risks data...

In mets: Analysis of Multivariate Event Times

Average Treatment effect for censored competing risks data using Binomial Regression

Description

Under the standard causal assumptions we can estimate the average treatment effect E(Y(1) - Y(0)). We need Consistency, ignorability ( Y(1), Y(0) indep A given X), and positivity.

Usage

binregATE(
  formula,
  data,
  cause = 1,
  time = NULL,
  beta = NULL,
  treat.model = ~+1,
  cens.model = ~+1,
  offset = NULL,
  weights = NULL,
  cens.weights = NULL,
  se = TRUE,
  type = c("II", "I"),
  kaplan.meier = TRUE,
  cens.code = 0,
  no.opt = FALSE,
  method = "nr",
  augmentation = NULL,
  outcome = c("cif", "rmst"),
  model = "exp",
  Ydirect = NULL,
  ...
)

Arguments

formula	formula with outcome (see coxph)
data	data frame
cause	cause of interest
time	time of interest
beta	starting values
treat.model	logistic treatment model given covariates
cens.model	only stratified cox model without covariates
offset	offsets for partial likelihood
weights	for score equations
cens.weights	censoring weights
se	to compute se's with IPCW adjustment, otherwise assumes that IPCW weights are known
type	"II" adds augmentation term, and "I" classic binomial regression
kaplan.meier	uses Kaplan-Meier for IPCW in contrast to exp(-Baseline)
cens.code	gives censoring code
no.opt	to not optimize
method	for optimization
augmentation	for augment binomial regression
outcome	can do CIF regression "cif"=F(t|X), "rmst"=E( min(T, t) | X) , or E( I(epsilon==cause) ( t - mint(T,t)) ) | X) depending on the number of the number of causes.
model	exp or linear model for E( min(T, t) | X)=exp(X^t beta), or E( I(epsilon==cause) ( t - mint(T,t)) ) | X)=exp(X^t beta)
Ydirect	use this outcome Y with IPCW vesion
...	Additional arguments to lower level funtions

Details

The first covariate in the specification of the competing risks regression model must be the treatment effect that is a factor. If the factor has more than two levels then it uses the mlogit for propensity score modelling. If there are no censorings this is the same as ordinary logistic regression modelling.

Estimates the ATE using the the standard binary double robust estimating equations that are IPCW censoring adjusted. Rather than binomial regression we also consider a IPCW weighted version of standard logistic regression logitIPCWATE.

Author(s)

Thomas Scheike

Examples

data(bmt)
dfactor(bmt)  <-  ~.

brs <- binregATE(Event(time,cause)~tcell.f+platelet+age,bmt,time=50,cause=1,
  treat.model=tcell.f~platelet+age)
summary(brs)

brsi <- binregATE(Event(time,cause)~tcell.f+tcell.f*platelet+tcell.f*age,bmt,time=50,cause=1,
  treat.model=tcell.f~platelet+age)
summary(brsi)

mets documentation built on April 4, 2025, 4:18 a.m.