kmi {kmi} | R Documentation |
The function performs a non parametric multiple imputation that aims at recovering the missing potential censoring times from competing events.
kmi(formula, data, id = NULL, etype, failcode = 1, nimp = 10, epsilon = 1, bootstrap = FALSE, nboot = 10)
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. |
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
.
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. |
This multiple imputation technique does not work for left-truncated data.
Arthur Allignol, arthur.allignol@fdm.uni-freiburg.de
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.
icu.pneu
, cox.kmi
,
Surv
, survfit
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)