Varying coefficient subdistribution regression for left-truncated semi-competing risks data

• Published in: Journal of multivariate analysis Vol. 131; pp. 65 - 78
• Main Authors: Li, Ruosha, Peng, Limin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2014; Taylor & Francis LLC
• Subjects: Biomedical research; Cumulative incidence; Data analysis; Hypothesis testing; Left truncation; Mathematical models; Observational studies; Probability distribution; Registry data analysis; Regression analysis; Studies; Time-varying coefficient; Cumulative incidence; Left truncation; Hypothesis testing; 62N01; 62P10; 62N02; Time-varying coefficient; Registry data analysis; Observational studies
• ISSN: 0047-259X; 1095-7243
• DOI: 10.1016/j.jmva.2014.06.005

Semi-competing risks data frequently arise in biomedical studies when time to a disease landmark event is subject to dependent censoring by death, the observation of which however is not precluded by the occurrence of the landmark event. In observational studies, the analysis of such data can be further complicated by left truncation. In this work, we study a varying coefficient subdistribution regression model for left-truncated semi-competing risks data. Our method appropriately accounts for the specifical truncation and censoring features of the data, and moreover has the flexibility to accommodate potentially varying covariate effects. The proposed method can be easily implemented and the resulting estimators are shown to have nice asymptotic properties. We also present inference, such as Kolmogorov–Smirnov type and Cramér–Von-Mises type hypothesis testing procedures for the covariate effects. Simulation studies and an application to the Denmark diabetes registry demonstrate good finite-sample performance and practical utility of the proposed method.