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

TL;DR

This paper develops a bivariate varying coefficient competing risks model to analyze how COVID-19 impacts hospital readmissions and deaths among dialysis patients over time, capturing complex dynamic effects.

Contribution

It introduces a novel bivariate varying coefficient model with tensor-product B-splines and an efficient algorithm for massive Medicare data analysis, addressing complex COVID-19 effect dynamics.

Findings

COVID-19 effects vary significantly over time and pandemic stages.

The proposed model accurately captures complex COVID-19 impact trajectories.

Simulation studies confirm the model's effectiveness and robustness.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. Motivated by a request by the U.S. Centers for Medicare & Medicaid Services, our analysis of their postdischarge hospital readmissions and deaths in 2020 revealed that the COVID-19 effect has varied significantly with postdischarge time and time since the onset of the pandemic. However, the complex dynamics of the COVID-19 effect trajectories cannot be characterized by existing varying coefficient models. To address this issue, we propose a bivariate varying coefficient model for competing risks within a cause-specific hazard framework, where tensor-product B-splines are used to estimate the surface of the COVID-19 effect. An efficient proximal Newton algorithm is developed to facilitate the fitting of the new model to the massive Medicare data for dialysis patients. Difference-based anisotropic penalization is introduced to mitigate model overfitting and the wiggliness of the estimated trajectories; various cross-validation methods are considered in the determination of optimal tuning parameters. Hypothesis testing procedures are designed to examine whether the COVID-19 effect varies significantly with postdischarge time and the time since pandemic onset, either jointly or separately. Simulation experiments are conducted to evaluate the estimation accuracy, type I error rate, statistical power, and model selection procedures. Applications to Medicare dialysis patients demonstrate the real-world performance of the proposed methods.