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

Archived Abstract of Former PSC Researcher

Kaciroti, N.A., Trivellore Raghunathan, M.A. Schork, and N.M. Clark. 2008. "A Bayesian model for longitudinal count data with non-ignorable dropout." Journal of the Royal Statistical Society: Series C (Applied Statistics), 57(5): 521-534.

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.

10.1111/j.1467-9876.2008.00628.x

PMCID: PMC2975948. (Pub Med Central)

Keywords:
Gibbs sampling, Longitudinal data, Non-linear mixed effects models, Poisson outcomes, Randomized trials, Transition Markov models

Browse | Search | Next

PSC In The News

RSS Feed icon

Shaefer comments on the Cares Act impact in negating hardship during COVID-19 pandemic

Heller comments on lasting safety benefit of youth employment programs

More News

Highlights

Dean Yang's Combatting COVID-19 in Mozambique study releases Round 1 summary report

Help Establish Standard Data Collection Protocols for COVID-19 Research

More Highlights


Connect with PSC follow PSC on Twitter Like PSC on Facebook