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 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.
Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, Colorado 80045, U.S.A.
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Juarez-Colunga
CEAUL and Department of Mathematics-IST, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal.
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Silva
Department of Statistical and Actuarial Sciences, Western University, Western Science Centre, London, Ontario N6A 5B7, Canada.
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Dean
D005260:Female; D020249:Hormone Replacement Therapy; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012720:Severity of Illness Index; D016896:Treatment Outcome
Bayesian analysis; Hurdle model; Longitudinal data; Poisson regression; Shared frailty model; Two-part model