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The coronavirus disease 2019 (COVID-19) pandemic has exerted a profoundimpact on patients with end-stage renal disease relying on kidney dialysis tosustain their lives. Motivated by a request by the U.S. Centers for Medicare &Medicaid Services, our analysis of their postdischarge hospital readmissionsand deaths in 2020 revealed that the COVID-19 effect has varied significantlywith postdischarge time and time since the onset of the pandemic. However, thecomplex dynamics of the COVID-19 effect trajectories cannot be characterized byexisting varying coefficient models. To address this issue, we propose abivariate varying coefficient model for competing risks within a cause-specifichazard framework, where tensor-product B-splines are used to estimate thesurface of the COVID-19 effect. An efficient proximal Newton algorithm isdeveloped to facilitate the fitting of the new model to the massive Medicaredata for dialysis patients. Difference-based anisotropic penalization isintroduced to mitigate model overfitting and the wiggliness of the estimatedtrajectories; various cross-validation methods are considered in thedetermination of optimal tuning parameters. Hypothesis testing procedures aredesigned to examine whether the COVID-19 effect varies significantly withpostdischarge time and the time since pandemic onset, either jointly orseparately. Simulation experiments are conducted to evaluate the estimationaccuracy, type I error rate, statistical power, and model selection procedures.Applications to Medicare dialysis patients demonstrate the real-worldperformance of the proposed methods.

Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients