A semivarying joint model for longitudinal binary and continuous outcomes.

ABSTRACT: 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 no

SUBMITTER: Kurum E 

PROVIDER: S-EPMC5033063 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature