# wglm: Logistic Regression Using IPCW In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

 wglm R Documentation

## Logistic Regression Using IPCW

### Description

Logistic regression over multiple timepoints where right-censoring is handle using inverse probability of censoring weighting.

### Usage

wglm(
regressor.event,
formula.censor,
times,
data,
cause = NA,
fitter = "coxph",
product.limit = FALSE
)

### Arguments

 regressor.event [formula] a formula with empty left hand side and the covariates for the logistic regression on the right hand side. formula.censor [formula] a formula used to fit the censoring model. times [numeric vector] time points at which to model the probability of experiencing an event. data [data.frame] dataset containing the time at which the event occured, the type of event, and regressors used to fit the censoring and logistic models. cause [character or numeric] the cause of interest. Defaults to the first cause. fitter [character] routine to fit the Cox regression models. product.limit [logical] if TRUE the survival is computed using the product limit estimator.

### Details

First, a Cox model is fitted (argument formula.censor) and the censoring probabilities are computed relative to each timepoint (argument times) to obtain the censoring weights. Then, for each timepoint, a logistic regression is fitted with the appropriate censoring weights and where the outcome is the indicator of having experience the event of interest (argument cause) at or before the timepoint.

### Value

an object of class "wglm".

### Examples

library(survival)

set.seed(10)
n <- 250
tau <- 1:5
d <- sampleData(n, outcome = "competing.risks")
dFull <- d[event!=0] ## remove censoring
dSurv <- d[event!=2] ## remove competing risk

#### no censoring ####
e.wglm <- wglm(regressor.event = ~ X1, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = dFull, product.limit = TRUE)
e.wglm ## same as a logistic regression

summary(ate(e.wglm, data = dFull, times = tau, treatment = "X1", verbose = FALSE))

#### right-censoring ####
## no covariante in the censoring model (independent censoring)
eC.wglm <- wglm(regressor.event = ~ X1, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = dSurv, product.limit = TRUE)
eC.wglm

## with covariates in the censoring model
eC2.wglm <- wglm(regressor.event = ~ X1 + X8,
formula.censor = Surv(time,event==0) ~ X1*X8,
times = tau, data = dSurv)
eC2.wglm

#### Competing risks ####
## here Kaplan-Meier as censoring model
eCR.wglm <- wglm(regressor.event = ~ X1,
formula.censor = Surv(time,event==0) ~ strata(X1),
times = tau, data = d, cause = 1, product.limit = TRUE)
eCR.wglm

riskRegression documentation built on March 23, 2022, 5:07 p.m.