title: Analysis of transplant urgency and benefit via multiple imputation.
abstract: 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.
authors: Novartis Institute of Biomedical Research, Cambridge, MA, 02139, U.S.A.
authors: Fang
authors: F
authors: Xiang
authors: Susan
authors: S
authors: Murray
authors: Xiaohong
authors: X
authors: Liu
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008171:Lung Diseases / Q000628:therapy*; D016040:Lung Transplantation / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D014481:United States; D014850:Waiting Lists
keywords: dependent censoring; multiple imputation; restricted mean life; survival; transplant benefit; transplant urgency