Using the Time-Varying Effect Model (TVEM) to Examine Dynamic Associations Between Daily Pain and Mood Assessments During 4-cycles of Chemotherapy for Gynecologic Cancer

2021 ◽  
Vol 22 (5) ◽  
pp. 596
Author(s):  
Jian Zhao ◽  
Heidi Donovan ◽  
Susan Sereika ◽  
Grace Campbell
Keyword(s):  
Gynecologic Cancer ◽  
Time Varying ◽  
Effect Model ◽  
Daily Pain

scholarly journals Modeling the complexity of dynamic, momentary interpersonal behavior: Applying the time-varying effect model to test predictions from interpersonal theory

2017 ◽  
Vol 68 ◽  
pp. 54-62 ◽  
Author(s):  
Sarah S. Dermody ◽  
Katherine M. Thomas ◽  
Christopher J. Hopwood ◽  
C. Emily Durbin ◽  
Aidan G.C. Wright
Keyword(s):  
Interpersonal Behavior ◽  
Interpersonal Theory ◽  
Time Varying ◽  
Effect Model

scholarly journals Treating co-occurring depression and anxiety: Modeling the dynamics of psychopathology and psychotherapy using the time-varying effect model.

2014 ◽  
Vol 82 (5) ◽  
pp. 839-853 ◽  
Author(s):  
Aidan G. C. Wright ◽  
Michael N. Hallquist ◽  
Holly A. Swartz ◽  
Ellen Frank ◽  
Jill M. Cyranowski
Keyword(s):  
Time Varying ◽  
Depression And Anxiety ◽  
Effect Model

scholarly journals Advancing the Understanding of Craving During Smoking Cessation Attempts: A Demonstration of the Time-Varying Effect Model

2013 ◽  
Vol 16 (Suppl_2) ◽  
pp. S127-S134 ◽  
Author(s):  
Stephanie T. Lanza ◽  
Sara A. Vasilenko ◽  
Xiaoyu Liu ◽  
Runze Li ◽  
Megan E. Piper
Keyword(s):  
Smoking Cessation ◽  
Time Varying ◽  
Effect Model

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

2015 ◽  
Vol 26 (6) ◽  
pp. 2812-2820 ◽  
Author(s):  
Songshan Yang ◽  
James A Cranford ◽  
Runze Li ◽  
Robert A Zucker ◽  
Anne Buu
Keyword(s):  
Gender Differences ◽  
Hypothesis Testing ◽  
Sample Size ◽  
Type I Error ◽  
Alternative Hypothesis ◽  
Error Rates ◽  
Type I ◽  
Time Varying ◽  
Time Points ◽  
Effect Model

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.

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

2016 ◽  
Vol 36 (5) ◽  
pp. 827-837 ◽  
Author(s):  
Songshan Yang ◽  
James A. Cranford ◽  
Jennifer M. Jester ◽  
Runze Li ◽  
Robert A. Zucker ◽  
...  
Keyword(s):  
Substance Abuse ◽  
Group Differences ◽  
Time Varying ◽  
Effect Model
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