Survival Analysis with Presence of Informative Censoring via Nonparametric Multiple Imputation

Abstract

?We propose a nonparametric multiple imputation approach to recover information for censored observations while analyzing survival data with presence of informative censoring. A working shared frailty model is proposed to estimate the magnitude of informative censoring, which is only used to determine the size of imputing risk set for each censored subject. We have shown that the distance between the posterior means of frailty is equivalent to the distance between the observed times. We, therefore, propose to use the observed times for subjects at risk to calculate the distance from each censored subject to select an imputing risk set for each censored subject. In simulation, we have shown the nonparametric multiple imputation approach produces survival estimates comparable to the targeted values and coverage rates comparable to the nominal level 95% even in a situation with a high degree of informative censoring. We have also demonstrated the approach on ACTG-175 and developed an alternative sensitivity analysis based on the approach for informative censoring.