cox.kmi: Cox proportional hazards model applied to imputed data sets

In aallignol/kmi: Kaplan-Meier Multiple Imputation for the Analysis of Cumulative Incidence Functions in the Competing Risks Setting

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, [e-mail]

See Also

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

Examples

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)
}

aallignol/kmi documentation built on June 4, 2019, 3:05 a.m.