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A time‐varying effect model for examining group differences in trajectories of zero‐inflated count outcomes with applications in substance abuse research
Author(s) -
Yang Songshan,
Cranford James A.,
Jester Jennifer M.,
Li Runze,
Zucker Robert A.,
Buu Anne
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7177
Subject(s) - null hypothesis , poisson distribution , sample size determination , statistics , zero (linguistics) , type i and type ii errors , mathematics , statistical hypothesis testing , statistical power , poisson regression , sample (material) , abstinence , group (periodic table) , econometrics , psychology , demography , physics , linguistics , psychiatry , sociology , quantum mechanics , thermodynamics , population , philosophy
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. Copyright © 2016 John Wiley & Sons, Ltd.