A time-varying effect model for studying gender differences in health behavior

Abstract

This study proposes a time-varying effect model that can be used to characterize gender-specific trajectories of health behaviors and conduct hypothesis testing for gender differences. The motivating examples demonstrate that the proposed model is applicable to not only multi-wave longitudinal studies but also short-term studies that involve intensive data collection. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size and the number of time points increase. In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all combinations of sample size and number of time points. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size and the number of time points are larger.

A time‐varying effect model for examining group differences in trajectories of zero‐inflated count outcomes with applications in substance abuse research

“…We also fill in the knowledge gap by comparing the power of the test in the zero component and the one in the Poisson component. Furthermore, the proposed model can be applied to not only multi-wave longitudinal studies like the MLS, but also short-term studies that involve intensive data collection such as daily process data [4, 13] and ecological momentary assessment (EMA) data [14, 15, 16, 17]. …”

“…A recent study by our group [4] proposed a time-varying effect model (TVEM) that explicitly characterizes gender differences in developmental trajectories of substance use by modeling gender as a time-varying effect. Such trajectories are estimated through non-parametric regression functions that do not assume fixed shapes like conventional growth curves.…”

Mathematical Programming for Statistical Inference

“…It forms the basis for decision science and all business-oriented decision-making methods. Yang et al [6] proposed a time-varying effect model that can be used to characterize genderspecific trajectories of health behaviours and conduct hypothesis testing for gender differences. In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all combinations of sample size and number of time points.…”