A semivarying joint model for longitudinal binary and continuous outcomes.
Semivarying models extend varying coefficient models by allowing some regression coefficients to be constant with respect to the underlying covariate(s). In this paper we develop a semivarying joint modelling framework for estimating the time-varying association between two intensively measured longitudinal response: a continuous one and a binary one. To overcome the major challenge of jointly modelling these responses, namely, the lack of a natural multivariate distribution, we introduce a Gaussian latent variable underlying the binary response. Then we decompose the model into two components: a marginal model for the continuous response, and a conditional model for the binary response given the continuous response. We develop a two-stage estimation procedure and discuss the asymptotic normality of the resulting estimators. We assess the finite-sample performance of our procedure using a simulation study, and we illustrate our method by analyzing binary and continuous responses from the Women's Interagency HIV Study.
Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520, USA.
Esra
E
Kürüm
Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
John
J
Hughes
Department of Statistics and The Methodology Center, The Pennsylvania State University, University Park, PA 16802-2111, USA.
Runze
R
Li
Generalized varying coefficient model; HIV; local linear regression; profile least squares
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