Longitudinal varying coefficient single-index model with censored covariates

• Published in: Biometrics Vol. 80; no. 1
• Main Authors: Wang, Shikun, Ning, Jing, Xu, Ying, Shih, Ya-Chen Tina, Shen, Yu, Li, Liang
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 29.01.2024
• Subjects: Aged; Biometric Practice; Computer Simulation; Humans; Male; Medicare; Models, Statistical; United States - epidemiology; United States; joint modeling; semiparametric model; seer-medicare; bivariate penalized spline; generalized estimating equations
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1093/biomtc/ujad006

It is of interest to health policy research to estimate the population-averaged longitudinal medical cost trajectory from initial cancer diagnosis to death, and understand how the trajectory curve is affected by patient characteristics. This research question leads to a number of statistical challenges because the longitudinal cost data are often non-normally distributed with skewness, zero-inflation, and heteroscedasticity. The trajectory is nonlinear, and its length and shape depend on survival, which are subject to censoring. Modeling the association between multiple patient characteristics and nonlinear cost trajectory curves of varying lengths should take into consideration parsimony, flexibility, and interpretation. We propose a novel longitudinal varying coefficient single-index model. Multiple patient characteristics are summarized in a single-index, representing a patient's overall propensity for healthcare use. The effects of this index on various segments of the cost trajectory depend on both time and survival, which is flexibly modeled by a bivariate varying coefficient function. The model is estimated by generalized estimating equations with an extended marginal mean structure to accommodate censored survival time as a covariate. We established the pointwise confidence interval of the varying coefficient and a test for the covariate effect. The numerical performance was extensively studied in simulations. We applied the proposed methodology to medical cost data of prostate cancer patients from the Surveillance, Epidemiology, and End Results-Medicare-Linked Database.