kmi (version 0.5.5)

cox.kmi: 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.

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.

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.

See Also

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

Examples

# NOT RUN {
data(icu.pneu)


if (require(survival)) {
    
    set.seed(1313)
    imp.dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
                   etype = event, id = id, failcode = 2, nimp = 5)
    
    fit.kmi <- cox.kmi(Surv(start, stop, event == 2) ~ pneu, imp.dat)
    
    summary(fit.kmi)
    
### Now using the censoring-complete data
    fit <- coxph(Surv(start, adm.cens.exit, event == 2) ~ pneu, icu.pneu)
    
    summary(fit)
    
    ## estimation of the censoring distribution adjusted on covariates
    dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
                    data = icu.pneu, etype = event, id = id,
                    failcode = 2, nimp = 5)
    
    fit.kmi2 <- cox.kmi(Surv(start, adm.cens.exit, event == 2) ~ pneu + age,
                        dat.cova)
    
    summary(fit.kmi2)
}
# }