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

Songshan Yang, James A. Cranford, Jennifer M. Jester, Runze Li, Robert A. Zucker, Anne Buu

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This study proposes a time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes. The motivating example demonstrates that this zero-inflated Poisson model allows investigators to study group differences in different aspects of substance use (e.g., the probability of abstinence and the quantity of alcohol use) simultaneously. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size increases; the accuracy under equal group sizes is only higher when the sample size is small (100). In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all settings. 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 is larger. Moreover, the hypothesis test for the group difference in the zero component tends to be less powerful than the test for the group difference in the Poisson component.

Original languageEnglish (US)
Pages (from-to)827-837
Number of pages11
JournalStatistics in Medicine
Volume36
Issue number5
DOIs
StatePublished - Feb 28 2017

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research'. Together they form a unique fingerprint.

Cite this