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Activity Number: 438
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract - #309356
Title: Analysis of Transplant Urgency and Benefit via Multiple Imputations
Author(s): Fang Xiang*+ and Susan Murray
Companies: Novartis and University of Michigan
Keywords: multiple imputation ; lung allocation score ; time-dependent censoring ; restricted mean model
Abstract:

Missing (censored) death times for lung disease 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 (MI) allows creation of complete datasets that can be used for a variety of standard analyses in this setting. We propose an MI approach to multiply impute lung candidate outcomes that incorporates time-varying factors predicting removal from the waitlist and estimates of transplant urgency via restricted mean models. Measures of transplant urgency and benefit for individual patient profiles are discussed in the context of Lung Allocation Score (LAS) modeling in the United States. Marginal survival estimates in the event that a transplant does not occur are also provided. Simulations suggest that the proposed MI method gives attractive results when compared to existing methods.


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