Analysis of transplant urgency and benefit via multiple imputation

Missing (censored) death times for lung candidates in urgent need of transplant are a signpost of success for allocation policy makers. However, statisticians analyzing these data must properly account for dependent censoring as the sickest patients are removed from the waitlist. Multiple imputation allows the creation of complete data sets that can be used for a variety of standard analyses in this setting. We propose an approach to multiply impute lung candidate outcomes that incorporates (i) time-varying factors predicting removal from the waitlist and (ii) estimates of transplant urgency via restricted mean models. The measures of transplant urgency and benefit for individual patient profiles are discussed in the context of lung allocation score modeling in the USA. Marginal survival estimates in the event that a transplant does not occur are also provided. Simulations suggest that the proposed imputation method gives attractive results when compared with existing methods.