cox.kmi {kmi}R Documentation

Cox proportional hazards model applied to imputed data sets

Description

This function fits Cox proportional hazards models to each imputed data set to estimate the regression coefficients in a proportional subdistribution hazards model, and pools the results.

Usage

cox.kmi(formula, imp.data, df.complete = Inf, ...)

Arguments

formula A formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
imp.data An object of class kmi.
df.complete Complete data degrees of freedom.
... Further arguments for the coxph function.

Details

Fits a Cox proportional hazards model on each imputed data set to estimate the regression coefficients in a proportional subdistribution hazards model, and pools the results, using the MIcombine function of the mitools package.

Value

An object of class cox.kmi including the following components:

coefficients Pooled regression coefficient estimates
variance Pooled variance estimate
nimp Number of multiple imputations
df degrees of freedom
call The matched call
individual.fit A list of coxph objects. One for each imputed data set.

Author(s)

Arthur Allignol, arthur.allignol@fdm.uni-freiburg.de

See Also

coxph, MIcombine, print.cox.kmi, summary.cox.kmi

Examples

data(icu.pneu)

set.seed(1313)
imp.dat <- kmi(Surv(entry, exit, status) ~ 1, data = icu.pneu,
               etype = event, id = id, failcode = 3, nimp = 5)

fit.kmi <- cox.kmi(Surv(entry, exit, event == 3) ~ pneu, imp.dat)

summary(fit.kmi)

### Now using the censoring-complete data
fit <- coxph(Surv(entry, adm.cens.exit, event == 3) ~ pneu, icu.pneu)

summary(fit)

[Package kmi version 0.3-2 Index]