2015
DOI: 10.1177/0962280215588224
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Zero-inflated count models for longitudinal measurements with heterogeneous random effects

Abstract: Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g., zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In these random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix … Show more

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Cited by 30 publications
(27 citation statements)
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“…ARR has been commonly used as a primary efficacy endpoint in phase 3 MS clinical trials . Because of data overdispersion, negative binomial regression is often used to analyze the data . In cases with excessive 0 counts, the ZIP and zero‐inflated negative binomial models are gaining popularity .…”
Section: Discussionmentioning
confidence: 99%
“…ARR has been commonly used as a primary efficacy endpoint in phase 3 MS clinical trials . Because of data overdispersion, negative binomial regression is often used to analyze the data . In cases with excessive 0 counts, the ZIP and zero‐inflated negative binomial models are gaining popularity .…”
Section: Discussionmentioning
confidence: 99%
“…() Zero‐inflated count models fit a mixture distribution, with a point mass at zero and a Poisson or negative binomial distribution for the “count” part. () Ignoring zero inflation in the data may yield biased estimates of fixed and random effects and, thus, may lead to inaccurate inferences for model parameters of interest …”
Section: Introductionmentioning
confidence: 99%
“…Wang et al (2002) discussed several basic methods for analysing drinking data (such as reducing the repeated measures of drinking over the whole study period to one summary for each subject, such as the proportion of drinking days or average alcohol consumption per day, and then analysing these univariate summaries, or using survival models to analyse time to the first (any or heavy) drinking day) and argued for the use of more sophisticated statistical methods (specifically, recurrent event survival analyses) that give a more comprehensive description of drinking behaviour over time. Other researchers (DeSantis et al, 2013;Zhu et al, 2017) proposed a longitudinal hurdle or zero-inflated Poisson model to analyse the zero-inflated average number of drinks per day (as opposed to the average number of drinks per drinking day) for each week (rounded to the nearest whole number). This artificially inflates the proportion of zeros by rounding and ignores the number of days in which drinking occurred during the week.…”
Section: Introductionmentioning
confidence: 99%
“…For subject B, who drinks heavily during the weekend (seven drinks on Saturday and seven on Sunday) and abstains for the rest of the week, we model the number of drinking days (2) and the average of seven drinks per drinking day. Previous literature using average drinks per day (DeSantis et al, 2013;Zhu et al, 2017) would solely model an average of 2 in both cases. Distinguishing between the two drinking patterns of subjects A and B is important because these two types of individual could have different future health and psychosocial outcomes, and treatments may be associated with different drinking patterns.…”
Section: Introductionmentioning
confidence: 99%