ArticleBiometrics2022
Stratified Cox models with time-varying effects for national kidney transplant patients: A new blockwise steepest ascent method.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions.Lifetime data analysis · 2026Article
- Flexible Modeling of Time-Dependent Covariate Effects in Survival Models With Correlated Competing Risks: Application to the Evaluation of Risk-Reducing Salpingo-Oophorectomy in Women With BRCA1 Pathogenic Variants.Statistics in medicine · 2026Article
- KULLBACK-LEIBLER-BASED DISCRETE FAILURE TIME MODELS FOR INTEGRATION OF PUBLISHED PREDICTION MODELS WITH NEW TIME-TO-EVENT DATASET.The annals of applied statistics · 2025Article
- Competing Risk Modeling with Bivariate Varying Coefficients to Understand the Dynamic Impact of COVID-19.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- surtvep: An R package for estimating time-varying effects.Journal of open source software · 2024Article
- Debiased lasso for stratified Cox models with application to the national kidney transplant data.The annals of applied statistics · 2023Article
- Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.Statistical methods in medical research · 2023Article
- Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients.Lifetime data analysis · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Analyzing the national transplant database, which contains about 300,000 kidney transplant patients treated in over 290 transplant centers, may guide the disease management and inform the policy of kidney transplantation. Cox models stratified by centers provide a convenient means to account for the clustered data structure, while studying more than 160 predictors with effects that may vary over time. As fitting a time-varying effect model with such a large sample size may defy any existing software, we propose a blockwise steepest ascent procedure by leveraging the block structure of parameters inherent from the basis expansions for each coefficient function. The algorithm iteratively updates the optimal blockwise search direction, along which the increment of the partial likelihood is maximized. The proposed method can be interpreted from the perspective of the minorization-maximization algorithm and increases the partial likelihood until convergence. We further propose a Wald statistic to test whether the effects are indeed time varying. We evaluate the utility of the proposed method via simulations. Finally, we apply the method to analyze the national kidney transplant data and detect the time-varying nature of the effects of various risk factors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.