A Two-step Estimation Approach for Logistic Varying Coefficient Modeling of Longitudinal Data.
Varying coefficient models are useful for modeling longitudinal data and have been extensively studied in the past decade. Motivated by commonly encountered dichotomous outcomes in medical and health cohort studies, we propose a two-step method to estimate the regression coefficient functions in a logistic varying coefficient model for a longitudinal binary outcome. The model depicts time-varying covariate effects without imposing stringent parametric assumptions. The proposed estimation is simple and can be conveniently implemented using existing statistical packages such as SAS and R. We study asymptotic properties of the proposed estimators which lead to asymptotic inference and also develop bootstrap inferential procedures to test whether the coefficient functions are indeed time-varying or are equal to zero. The proposed methodology is illustrated with the analysis of a smoking cessation data set. Simulations are used to evaluate the performance of the proposed method compared to an alternative estimation method based on local maximum likelihood.
University of California, Los Angeles.
Jun
J
Dong
University of California, Los Angeles.
Jason P
JP
Estes
University of California, Los Angeles.
Gang
G
Li
University of California, Los Angeles.
Damla
D
Şentürk
Local maximum likelihood; Logistic regression; Longitudinal binary data; Smoothing; Time-varying effects