pseudo_independent: Compute pseudo observations under independent censoring

In eventglm: Regression Models for Event History Outcomes

Compute pseudo observations under independent censoring

Description

Assuming completely independent censoring, i.e., censoring does not depend on the survival time nor any covariates in the model, the pseudo observations are calculated with the standard jackknife approach

Usage

pseudo_independent(
  formula,
  time,
  cause = 1,
  data,
  type = c("cuminc", "survival", "rmean"),
  formula.censoring = NULL,
  ipcw.method = NULL
)

Arguments

formula	A formula specifying the model. The left hand side must be a Surv object specifying a right censored survival or competing risks outcome. The status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death). For competing risks, the event variable will be a factor, whose first level is treated as censoring. The right hand side is the usual linear combination of covariates.
time	Numeric constant specifying the time at which the cumulative incidence or survival probability effect estimates are desired.
cause	Numeric or character constant specifying the cause indicator of interest.
data	Data frame in which all variables of formula can be interpreted.
type	One of "survival", "cuminc", or "rmean"
formula.censoring	Not used with this method, see pseudo_stratified, pseudo_aareg or pseudo_coxph
ipcw.method	Not used with this method

Value

A vector of jackknife pseudo observations

Examples

POi <- pseudo_independent(Surv(time, status) ~ 1, 1500, cause = 1, data = colon, type = "survival")
mean(POi)

eventglm documentation built on April 3, 2025, 6:23 p.m.