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Joint modeling of zero‐inflated panel count and severity outcomes

Author

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  • E. Juarez‐Colunga
  • G. L. Silva
  • C. B. Dean

Abstract

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.

Suggested Citation

  • E. Juarez‐Colunga & G. L. Silva & C. B. Dean, 2017. "Joint modeling of zero‐inflated panel count and severity outcomes," Biometrics, The International Biometric Society, vol. 73(4), pages 1413-1423, December.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1413-1423
    DOI: 10.1111/biom.12691
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    References listed on IDEAS

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    1. Benjamin French & Patrick J. Heagerty, 2009. "Marginal Mark Regression Analysis of Recurrent Marked Point Process Data," Biometrics, The International Biometric Society, vol. 65(2), pages 415-422, June.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
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    Cited by:

    1. Dexen DZ. Xi & C.B. Dean & Stephen W. Taylor, 2020. "Modeling the duration and size of extended attack wildfires as dependent outcomes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.

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