Multiply imputing informatively censored time-to-event data

Session

While noninformative censoring is plausible when censoring is due to end of study, it is less plausible when censoring is due to loss to follow-up. Sensitivity analyses for departures from the noninformative censoring assumption can be performed using multiple imputation under the Cox model. These have been implemented in R but are not commonly used. We propose a new implementation in Stata.

Our existing stsurvimpute command (on SSC) imputes right-censored data under noninformative censoring, using a flexible parametric survival model fit by stpm2. We extend this to allow a sensitivity parameter gamma, representing the log of the hazard ratio in censored individuals versus comparable uncensored individuals (the informative censoring hazard ratio, ICHR). The sensitivity parameter can vary between individuals, and imputed data can be recensored at the end-of-study time. Because the mi suite does not allow imputed variables to be stset, we create an imputed data set in ice format and analyze it using mim.

In practice, sensitivity analysis computes the treatment effect for a range of scientifically plausible values of gamma. We illustrate the approach using a cancer clinical trial.

References:
Jackson D., I. R. White, S. Seaman, H. Evans, K. Baisley, J. Carpenter. 2014. Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation. Statistics in medicine. 33: 4681–4694.