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Activity Number: 87 - Survival and Longitudinal/Clustered Data Analysis
Type: Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Biometrics Section
Abstract #319022
Title: Risk Factors for Graft Failure: Analyzing National Kidney Transplant Data with Debiased Lasso via Quadratic Programming for Stratified Cox Models
Author(s): Lu Xia* and Bin Nan and Yi Li
Companies: University of Washington, Seattle and University of California Irvine and University of Michigan
Keywords: Confidence intervals; Diverging number of covariates; End-stage renal disease; Graft failure free survival; Statistical inference

The Scientific Registry of Transplant Recipients (SRTR) system has become a rich resource for understanding the complex mechanisms of graft failure. As patients received care from different transplant centers, which might strongly confound graft failure mechanisms, Cox models stratified by centers can eliminate their confounding effects. Also, since recipient age is a proven non-modifiable risk factor, a common practice is to fit models separately by recipient age groups. The moderate sample sizes, relative to the number of covariates, in some age groups may lead to biased maximum stratified partial likelihood estimates and unreliable confidence intervals even when samples still outnumber covariates. To draw reliable inference on a comprehensive list of risk factors measured from both donors and recipients in SRTR, we propose a de-biased lasso approach via quadratic programming for fitting stratified Cox models. We establish asymptotic properties and verify via simulations that our method produces consistent estimates and confidence intervals with nominal coverage probabilities. The results can aid in delineating associations of many clinical factors with graft failure.

Authors who are presenting talks have a * after their name.

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