Assessing Sexual Attitudes and Behaviors of Young Women: A Joint Model with Nonlinear Time Effects, Time Varying Covariates, and Dropouts

• 成果类型: Article
• 署名作者: Ghosh, Pulak; Tu, Wanzhu
• 署名单位: University System of Georgia; Georgia State University; Indiana University System; Indiana University Indianapolis; Regenstrief Institute Inc
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0013
• 发表日期: 2009
• 页码: 474-485
• 关键词: zero-inflated poisson; clustered data; parameter expansion; regression-models; bayesian-analysis; longitudinal data; adolescent women; Missing Data; count data; trials

摘要

Understanding human sexual behaviors is essential for the effective prevention of sexually transmitted infections (STI). Analysis of longitudinally measured sexual behavioral data, however, is often complicated by zero-inflation of event counts, nonlinear time trend, time-varying covariates, and informative dropouts. Ignoring these complicating factors could undermine the validity of the study findings. In this article, we put forth a unified joint modeling structure that accommodates these features of the data. Specifically, we propose a pair of simultaneous models for the zero-inflated event counts: Each of these models contains an auto-regressive structure for the accommodation of the effect of recent event history, and a nonparametric compenent for the modeling of nonlinear time effect. Informative dropout and time varying covariates are modeled explicitly in the process. Model fitting and parameter estimation are carried out in a Bayesian paradigm by the use of a Markov chain Monte Carlo (MCMC) method. Analytical results showed that adolescent sexual behaviors tended to evolve nonlinearly over time, and they were strongly influenced by the day-to-day variations in mood and sexual interests. These findings suggest that adolescent sex is. to a large extent, driven by intrinsic factors rather than being compelled by circumstances. thus highlighting the need of education on self-protective measures against infection risks.