Open Access
September 2024 Evaluation of transplant benefits with the U.S. Scientific Registry of Transplant Recipients by semiparametric regression of mean residual life
Ge Zhao, Yanyuan Ma, Huazhen Lin, Yi Li
Author Affiliations +
Ann. Appl. Stat. 18(3): 2403-2423 (September 2024). DOI: 10.1214/24-AOAS1887

Abstract

Kidney transplantation is the most effective renal replacement therapy for end stage renal disease patients. With the severe shortage of kidney supplies and for the clinical effectiveness of transplantation, patient’s life expectancy posttransplantation is used to prioritize patients for transplantation; however, severe comorbidity conditions and old age are the most dominant factors that negatively impact posttransplantation life expectancy, effectively precluding sick or old patients from receiving transplants. It would be crucial to design objective measures to quantify the transplantation benefit by comparing the mean residual life with and without a transplant, after adjusting for comorbidity and demographic conditions. To address this urgent need, we propose a new class of semiparametric covariate-dependent mean residual life models. Our method estimates covariate effects semiparametrically efficiently and the mean residual life function nonparametrically, enabling us to predict the residual life increment potential for any given patient. Our method potentially leads to a more fair system that prioritizes patients who would have the largest residual life gains. Our analysis of the kidney transplant data from the U.S. Scientific Registry of Transplant Recipients also suggests that a single index of covariates summarize well the impacts of multiple covariates, which may facilitate interpretations of each covariate’s effect. Our subgroup analysis further disclosed inequalities in survival gains across groups defined by race, gender and insurance type (reflecting socioeconomic status).

Funding Statement

G. Zhao was supported by Faculty Development Program, Portland State University (FEAGXZ). H. Lin was supported by NSFC (No. 11931014 and 11829101).

Acknowledgments

The authors deeply thank Editor Fan Li and two anonymous referees for their insightful and detailed comments that helped substantially improve the quality of the manuscript.

Citation

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Ge Zhao. Yanyuan Ma. Huazhen Lin. Yi Li. "Evaluation of transplant benefits with the U.S. Scientific Registry of Transplant Recipients by semiparametric regression of mean residual life." Ann. Appl. Stat. 18 (3) 2403 - 2423, September 2024. https://doi.org/10.1214/24-AOAS1887

Information

Received: 1 October 2023; Revised: 1 February 2024; Published: September 2024
First available in Project Euclid: 5 August 2024

Digital Object Identifier: 10.1214/24-AOAS1887

Keywords: kidney transplant , mean residual life , nonparametric estimation , semiparametric efficient estimator

Rights: Copyright © 2024 Institute of Mathematical Statistics

Vol.18 • No. 3 • September 2024
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