Multiple imputation for informatively censored time to event data – the InformativeCensoring R package

The R package InformativeCensoring, available on CRAN, can be used to perform multiple imputation for a time to event outcome when it is believed censoring may be informative.

Two methods are implemented. The first, based on Jackson et al 2014, first fits a Cox model to the observed data under the usual (conditional on covariates) non-informative censoring assumption. Multiple imputed datasets can then be generated in which it is assumed that the hazard for failure following censoring changes by a user specified multiplier compared to the hazard implied by the non-informative censoring assumption.

The second, based on Hsu and Taylor, performs Kaplan-Meier type imputation of censored time, in which the Kaplan-Meier estimate is calculated for an individual based on data from those individuals who are closest in terms of predicted hazard of failure and predicted hazard of censoring. This matching can also be performed using time-dependent covariates. The approach thus assumes that censoring is non-informative conditional on the covariates used to predict the hazard of failure/censoring.

Corresponding vignettes are provided describing how the two methods are implemented and can be used.

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