Joint Modeling of Zero-Inflated Panel Count and Severity Outcomes

Panel counts are often encountered in longitudinal, such as diary, studies where individuals are followed over time and the number of events occurring in time intervals, or panels, is recorded. This article develops methods for situations where, in addition to the counts of events, a mark, denoting...

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Bibliographic Details
Published inBiometrics Vol. 73; no. 4; pp. 1413 - 1423
Main Authors Juarez-Colunga, E., Silva, G. L., Dean, C. B.
Format Journal Article
LanguageEnglish
Published United States Wiley-Blackwell 01.12.2017
Blackwell Publishing Ltd
Subjects
Bayesian analysis
BIOMETRIC PRACTICE
Computer simulation
Hurdle model
Hysterectomy
Longitudinal data
Markov analysis
Markov chains
Modelling
Monte Carlo simulation
Ovariectomy
Poisson regression
Shared frailty model
Two‐part model
Two-part model
Longitudinal data
Bayesian analysis
Shared frailty model
Hurdle model
Poisson regression
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Summary:Panel counts are often encountered in longitudinal, such as diary, studies where individuals are followed over time and the number of events occurring in time intervals, or panels, is recorded. This article develops methods for situations where, in addition to the counts of events, a mark, denoting a measure of severity of the events, is recorded. In many situations there is an association between the panel counts and their marks. This is the case for our motivating application that studies the effect of two hormone therapy treatments in reducing counts and severities of vasomotor symptoms in women after hysterectomy/ovariectomy. We model the event counts and their severities jointly through the use of shared random effects. We also compare, through simulation, the power of testing for the treatment effect based on such joint modeling and an alternative scoring approach, which is commonly employed. The scoring approach analyzes the compound outcome of counts times weighted severity. We discuss this approach and quantify challenges which may arise in isolating the treatment effect when such a scoring approach is used. We also show that the power of detecting a treatment effect is higher when using the joint model than analysis using the scoring approach. Inference is performed via Markov chain Monte Carlo methods.
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ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12691