kmi {kmi}R Documentation

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

formula A formula object, that must have a Surv object on the left of a ~ operator. The right hand side should be 1.
data A data.frame in which to interpret the variables given in the formula, etype and id. It is mandatory.
id Used to identify individual subjects when one subject can have several rows of data, e.g., with time-dependent covariates. Set to NULL when there is only one raw of data per subject.
etype Variable specifying the type of competing event. When status == 1 in formula, etype describes the type of event, otherwise, for censored observation, (status == 0), the value of etype is ignored.
failcode Indicates the failure cause of interest. Imputation will be performed on the other competing events. Default is 1.
nimp Number of multiple imputation. Default is 10.
epsilon When the last time is an event, a censoring time equal to max(time) + epsilon is added. By default, epsilon is set to 1.
bootstrap Logical. Whether to estimate the censoring distribution using bootstrap samples. Default is FALSE.
nboot If bootstrap is set to TRUE, nboot determines the number of bootstrap samples.

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.

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

Value

An object of class kmi with the following components:

imputed.data A list of matrices giving the imputed times in the first column and imputed event type in the second column. The event status for imputed times take value 0 (censored).
original.data The original data set
info Gives the names of the time and event indicator column in the original data set.
call The matched call.

Note

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

Author(s)

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

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.

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)

dat <- kmi(Surv(entry, exit, status) ~ 1, data = icu.pneu,
           etype = event, id= id, failcode = 3, 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(entry, exit, ev != 0) ~ 1, data = icu.pneu,
           etype = ev, id= id, failcode = 3, nimp = 5)

[Package kmi version 0.3-2 Index]