Statistical Methods for Organ Transplant

Abstract

In this dissertation, we propose novel statistical methods to improve clinical decision support for organ transplant donors and recipients, using data from the United Network for Organ Sharing national registry. In our first project, we develop a feature selection method for support vector regression in order to benefit from the method’s flexibility while combating overfitting. Support vector regression is advantageous due to its use of a kernel for flexibility and computational efficiency; penalized methods for feature selection limit the choice in kernel to finite dimensional transformations and are thus insufficient. We propose a novel feature selection method for support vector regression based on a genetic algorithm that iteratively searches across potential subsets of covariates to find those that yield the best performance according to a user-defined fitness function. We apply our method to predict donor kidney function one year after transplant. In our second project, we develop an estimator for marginal survival under a dynamic treatment regime for organ transplant, where treatment is defined as the patient’s decision to accept or decline an organ when it is offered to them. We apply our method to kidney transplant patients to recommend thresholds of the quality of organ for acceptance. In our third project, we again utilize the genetic algorithm’s flexible optimization, this time to identify optimal treatment regimes. We define the treatment regime as a decision list in order to develop our method. We apply our method to identify treatment regimes for liver transplant patients who may wish to undergo a simultaneous kidney transplant. Overall, we develop novel methods in diverse fields of statistics tailored for the organ transplantation context, and we demonstrate their performance and meaningful clinical implications via simulations and real data examples.