Assessing Sexual Attitudes and Behaviors of Young Women: A Joint Model with Nonlinear Time Effects, Time Varying Covariates, and Dropouts
年轻女性性态度与行为的联合模型：非线性时间效应、时变协变量与退出AI 解读 ↓
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年轻女性性态度与行为的联合模型：非线性时间效应、时变协变量与退出
- 研究问题
- 如何分析具有零膨胀计数、非线性时间趋势、时变协变量和信息性退出的纵向性行为数据，并描述行为随时间的变化？
- 方法与思路
- 作者构造联合模型，以带自回归结构和非参数时间项的联立模型处理零膨胀计数，显式建模退出与时变协变量，并用贝叶斯 MCMC 估计。
- 材料陈述的贡献
- 摘要提出容纳上述复杂因素的统一框架，报告行为随时间非线性演化且与日常情绪、性兴趣变化有关，并讨论感染预防教育含义。
- 阅读关注点
- 应核对联合模型各部分的联系、信息性退出的假设及可识别性，并检查先验、MCMC 诊断与关于情绪和兴趣的解释依据。
- 阅读边界
- OpenAlex 索引摘要未注明稿件版本；退出机制与模型假设未展开，观察性关联不足以确认内在因素的因果作用，也不能推断刊本变化。
原文摘要
Understanding human sexual behaviors is essential for the effective prevention of sexually transmitted infections. 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 paper, 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 component 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.