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A Bayesian model for longitudinal count data with non‐ignorable dropout
Author(s) -
Kaciroti Niko A.,
Raghunathan Trivellore E.,
Anthony Schork M.,
Clark Noreen M.
Publication year - 2008
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2008.00628.x
Subject(s) - missing data , dropout (neural networks) , bayesian probability , sensitivity (control systems) , count data , computer science , event (particle physics) , statistics , mixture model , longitudinal data , data mining , econometrics , mathematics , machine learning , poisson distribution , physics , quantum mechanics , electronic engineering , engineering
Summary. 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.