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A Bayesian model for longitudinal count data with non-ignorable dropout

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  • Niko A. Kaciroti
  • Trivellore E. Raghunathan
  • M. Anthony Schork
  • Noreen M. Clark

Abstract

Asthma is an important chronic disease of childhood. An intervention programme for managing asthma was designed on principles of self-regulation and was evaluated by a randomized longitudinal study. The study focused on several outcomes, and, typically, missing data remained a pervasive problem. We develop a pattern-mixture model to evaluate the outcome of intervention on the number of hospitalizations with non-ignorable dropouts. Pattern-mixture models are not generally identifiable as no data may be available to estimate a number of model parameters. Sensitivity analyses are performed by imposing structures on the unidentified parameters. We propose a parameterization which permits sensitivity analyses on clustered longitudinal count data that have missing values due to non-ignorable missing data mechanisms. This parameterization is expressed as ratios between event rates across missing data patterns and the observed data pattern and thus measures departures from an ignorable missing data mechanism. Sensitivity analyses are performed within a Bayesian framework by averaging over different prior distributions on the event ratios. This model has the advantage of providing an intuitive and flexible framework for incorporating the uncertainty of the missing data mechanism in the final analysis. Copyright (c) 2008 Royal Statistical Society.

Suggested Citation

  • Niko A. Kaciroti & Trivellore E. Raghunathan & M. Anthony Schork & Noreen M. Clark, 2008. "A Bayesian model for longitudinal count data with non-ignorable dropout," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 521-534.
  • Handle: RePEc:bla:jorssc:v:57:y:2008:i:5:p:521-534
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    References listed on IDEAS

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    1. M. G. Kenward, 2003. "Pattern-mixture models with proper time dependence," Biometrika, Biometrika Trust, vol. 90(1), pages 53-71, March.
    2. Paul S. Albert & Dean A. Follmann, 2000. "Modeling Repeated Count Data Subject to Informative Dropout," Biometrics, The International Biometric Society, vol. 56(3), pages 667-677, September.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    4. Kaciroti, Niko A. & Raghunathan, Trivellore E. & Schork, M. Anthony & Clark, Noreen M. & Gong, Molly, 2006. "A Bayesian Approach for Clustered Longitudinal Ordinal Outcome With Nonignorable Missing Data: Evaluation of an Asthma Education Program," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 435-446, June.
    5. Wensheng Guo & Sarah J. Ratcliffe & Thomas Ten T. Have, 2004. "A Random Pattern-Mixture Model for Longitudinal Data With Dropouts," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 929-937, December.
    6. Michael J. Daniels & Joseph W. Hogan, 2000. "Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout," Biometrics, The International Biometric Society, vol. 56(4), pages 1241-1248, December.
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