Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples
The function performs a non parametric multiple imputation that aims at recovering the missing potential censoring times from competing events.
1 2 |
formula |
A |
data |
A data.frame in which to interpret the variables given in
the formula, |
id |
Used to identify individual subjects when one subject can
have several rows of data, e.g., with time-dependent covariates. Set
to |
etype |
Variable specifying the type of competing event. When
|
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 |
bootstrap |
Logical. Whether to estimate the censoring distribution using bootstrap samples. Default is FALSE. |
nboot |
If |
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
.
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. |
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.
This multiple imputation technique does not work for left-truncated data.
Arthur Allignol, arthur.allignol@gmail.com
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.
icu.pneu
, cox.kmi
,
Surv
, survfit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | 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)
}
|
Loading required package: survival
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