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

## Description

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

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```library(survival) set.seed(10) n <- 250 tau <- 1:5 d <- sampleData(n, outcome = "competing.risks") d\$Y <- (d\$event == 1)*(d\$time <= tau) d0 <- d[event!=0] ## remove censoring ## no censoring e0.wglm <- wglm(regressor.event = ~ X1, formula.censor = Surv(time,event==0) ~ X1, times = tau, data = d0) e0.glm <- glm(Y ~ X1, family = binomial, data = d0) ## censoring e.wglm <- wglm(regressor.event = ~ X1, formula.censor = Surv(time,event==0) ~ X1, times = tau, data = d) ```

riskRegression documentation built on Jan. 13, 2021, 11:12 a.m.