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

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:

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

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)
}

Hospital acquired penumonia in ICU

Description

This data set is a random sample drawn from the SIR-3 study that aimed at analysing the effect of nosocomial infections on the length of ICU stay. Patients were included in the study if they had stayed at least 1 day in the unit. The sample includes information to assess the effect of nosocomial pneumonia on the length of stay. The endpoint is either discharge alive from the ICU or dead in the unit. These data are censoring complete as the censoring time is known for all patients.

Usage

data(icu.pneu)

Format

A data frame with 1421 observations on the following 8 variables.

id

Individual patient id.

start

Start of the observation time.

stop

Failure time.

status

Censoring status. 0 if the observation is censored, 1 otherwise.

event

Event type. 2 is death in ICU, 3 is discharge alive

pneu

Nosocomial pneumonia indicator.

adm.cens.exit

Exit times for patients discharged alive are replaced by their administrative censoring times.

age

Age at inclusion

sex

Sex. F for female and M for male

Source

Beyersmann, J., Gastmeier, P., Grundmann, H., Baerwolff, S., Geffers, C., Behnke, M., Rueden, H., and Schumacher, M. Use of multistate models to assess prolongation of intensive care unit stay due to nosocomial infection. Infection Control and Hospital Epidemiology, 27:493-499, 2006.

References

Beyersmann, J. and Schumacher, M. (2008). Time-dependent covariates in the proportional hazards model for competing risks. Biostatistics, 9:765–776.

Examples

data(icu.pneu)

Kaplan-Meier Multiple Imputation for Competing Risks

Description

The function performs a non parametric multiple imputation that aims at recovering the missing potential censoring times from competing events.

Usage

kmi(formula, data, id = NULL, etype, failcode = 1, nimp = 10,
    epsilon = 1, bootstrap = FALSE, nboot = 10)

Arguments

Details

It was shown that if censoring times are observed for all individuals, methods for standard right-censored survival data can be used to analyse cumulative incidence functions from competing risks (Fine and Gray 1999). Therefore the idea proposed by Ruan and Gray (2008) is to impute potential censoring times for individuals who have failed from the competing events. The censoring times are imputed from the conditional Kaplan-Meier estimator of the censoring distribution.

Estimation of the censoring distribution may be improved through bootstrapping. Estimation might also be improved fitting a model for the censoring distribution. When covariates are given, a proportional hazards model on the hazard of censoring is fit. The censoring times are then imputed from the estimated model.

The competing risks model formulation in formula mimics the one in survfit.

Value

An object of class kmi with the following components:

Warning

When a proportional hazards model is fit for modelling the censoring distribution, the censoring times are imputed from the imputed model. When there is missing covariate information for the prediction, mean imputation is used.

Note

This multiple imputation technique does not work for left-truncated data.

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

References

Ruan, P.K. and Gray, R.J. (2008). Analyses of cumulative incidence functions via non-parametric multiple imputation. Statistics in Medicine, 27(27):5709–5724.

Allignol, A. and Beyersmann, J. (2010). Software for fitting nonstandard proportional subdistribution hazards models. Biostatistics, doi:10.1093/biostatistics/kxq018

Fine, J.P. and Gray, R.J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 94(446):496–509.

See Also

icu.pneu, cox.kmi, Surv, survfit

Examples

data(icu.pneu)

if (require(survival)) {
    
    dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
               etype = event, id= id, failcode = 2, nimp = 5)
    
    ## another way to specify the formula if there is no status
    ## variable
    icu.pneu$ev <- icu.pneu$event
    icu.pneu$ev[icu.pneu$status == 0] <- 0
    
    dat <- kmi(Surv(start, stop, ev != 0) ~ 1, data = icu.pneu,
               etype = ev, id= id, failcode = 2, nimp = 5)
    
    ## with covariates to model the censoring distribution
    dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
                    data = icu.pneu, etype = event, id = id,
                    failcode = 2, nimp = 5)
    
}

Print method for cox.kmi objects

Description

Print method for cox.kmi objects.

Usage

## S3 method for class 'cox.kmi'
print(x, print.ind = FALSE, ...)

Arguments

Value

No value returned

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

See Also

cox.kmi, summary.cox.kmi

Print method for summary.cox.kmi objects

Description

Print method for summary.cox.kmi objects.

Usage

## S3 method for class 'summary.cox.kmi'
print(x,
digits = max(getOption("digits") - 3, 3),
signif.stars = getOption("show.signif.stars"),
print.ind = FALSE, ...)

Arguments

Value

No value returned

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

See Also

summary.cox.kmi

Summary method for cox.kmi objects

Description

Provides a summary of the fitted model.

Usage

## S3 method for class 'cox.kmi'
summary(object, conf.int = 0.95, scale = 1, ...)

Arguments

Value

An object of class summary.cox.kmi with the following components:

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

See Also

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