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Time-varying effects of predictors of sexual risk behavior in adolescents and young adults

Time-varying effects of predictors of sexual risk behavior in adolescents and young adults. Sara A. Vasilenko, Stephanie T. Lanza, Runze Li & Jennifer S. Barber. Outline. Background on time-varying processes in sexual behavior Time-varying effect model (TVEM) Examples Add Health RDSL

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Time-varying effects of predictors of sexual risk behavior in adolescents and young adults

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  1. Time-varying effects of predictors of sexual risk behavior in adolescents and young adults Sara A. Vasilenko, Stephanie T. Lanza, Runze Li & Jennifer S. Barber

  2. Outline • Background on time-varying processes in sexual behavior • Time-varying effect model (TVEM) • Examples • Add Health • RDSL • Summary and Implications

  3. Background • Meaning and riskiness of sexual behavior can vary over time • Adolescence v. Midlife • Time in a relationship

  4. Background • Traditional methods don’t account for these time-varying processes • Collapse across age, divide into groups • Changes occur in continuous time

  5. TVEM • Time-varying effect model (Tan et al., 2012; Shiyko et al., 2012) • Flexible, nonparametric method for analyzing time-varying effects • Versions for continuous, dichotomous, zero-inflated Poisson outcomes • Logistic TVEM (dichotomous) presented • Macro available at methodology.psu.edu

  6. Logistic TVEM • where

  7. Example 1 Time-varying predictors of risky sex over developmental time

  8. Sample Questions • How do odds of having multiple partners change over time from early adolescence to young adulthood? • How does the association between heavy episodic drinking and multiple partners change over time? • How do these differ by gender?

  9. Method • Data from 4 waves of Add Health (ages 12 to 32) • Participants in 7th to 12th grade during first wave of study, with follow-up interviews 1 year later, 7 years later, and 13 years later • Contractual data; N=12,051 with 39,063 total person-records

  10. Data Preparation

  11. Measures • Outcome: Multiple partners in past year • Predictor • Past year Heavy Episodic Drinking (Any/None)

  12. Female 95% CI Female Male 95% CI Male

  13. Female 95% CI Female Male 95% CI Male

  14. Example 2 Time-varying predictors of risky sex over time in a relationship

  15. Sample Questions • How do odds of using a condom change over time from the first to 120th week of a relationship? • How does the association between contraceptive attitudes and condom use change over time?

  16. Method • Data from the Relationship Dynamics and Social Life (RDSL) Study • 1,003 women aged 18-20 (35% African American, Mage=18.7) • Followed weekly for 2.5 years • Up to 130 occasions per person • Used occasions when in a relationship between 0 and 130 weeks in duration • 29,823 occasions, 608 individuals

  17. Measures • Outcome: Weekly condom use • Predictors • Baseline Contraceptive Attitudes (6-item scale)

  18. Estimate 95% CI

  19. Estimate 95% CI

  20. Summary • Rates and predictors of risky sexual behavior can change over time • TVEM can help uncover processes unfolding in continuous time • Prevention programs should target predictors relevant to individuals’ ages and stages of a relationship

  21. Acknowledgments • Grants 2T32DA 017629 and P50-DA010075-17 • Add Health funded by: P01-HD31921 • RDSL funded by: R01 HD 050329 • Thanks to Nicole Butera, John Dziak, YasminKusunaki, Michael Yang

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