Assessing sexual attitudes and behaviors of young women: A joint model with nonlinear time effects, time varying covariates, and dropouts

Pulak Ghosh, Wanzhu Tu

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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, timevarying 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 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.

Original languageEnglish
Pages (from-to)474-485
Number of pages12
JournalJournal of the American Statistical Association
Volume104
Issue number486
DOIs
StatePublished - Jun 2009

Fingerprint

Time-varying Covariates
Joint Model
Drop out
Informative Dropout
Infection
Count
Zero-inflation
Joint Modeling
Mood
Model Fitting
Markov Chain Monte Carlo Methods
Human Behavior
Parameter Estimation
Covariates
Time-varying
Paradigm
Zero
Modeling
Model
Young women

Keywords

  • Joint modeling
  • Markov Chain Monte Carlo
  • Mood
  • Sexually transmitted infections
  • Zero-inflated Poisson

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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AB - 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, timevarying 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 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.

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