Sensitivity analyses for informative censoring in survival data: A trial example

In a controlled clinical trial comparing an experimental drug to a control using time to event analysis, the logrank test is normally used to test against the equality between two survival curves when the proportional hazard rate assumption is held, which of course requires non-informative censoring. The authors used an example from a randomized, double-blind, parallel group, low-dose active controlled study comparing the safety and efficacy of two doses (400 mg/day versus 50 mg/day) of study medication used as monotherapy for the treatment of newly diagnosed or recurrent epilepsy. This analysis imputes the event time of subjects considered to have problematic informative censoring to demonstrate the impact of violations in necessary assumptions, and assesses robustness of the p-value as calculated from imputed data as compared with un-imputed data. Assuming a parametric distribution for time to event, had these subjects resulted in an event in the trial after withdrawal, the expected additional time to event is formulated and calculated using methods developed in this article. Combining the imputed informative censoring subjects with the remainder of the original data, new p-values are obtained using the log-rank test and compared to the original p-value. KM plots are also compared.