Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Stephenson assessing in-home HIV testing and counseling for male couples

Thompson says mass incarceration causes collapse of Detroit neighborhoods

Liberal-conservative gap by education level growing in U.S.

Highlights

Maggie Levenstein named director of ISR's Inter-university Consortium for Political and Social Research

Arline Geronimus receives 2016 Harold R. Johnson Diversity Service Award

PSC spring 2016 newsletter: Kristin Seefeldt, Brady West, newly funded projects, ISR Runs for Bob, and more

AAUP reports on faculty compensation by category, affiliation, and academic rank

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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

Publication Abstract

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), 57:521-534.

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.

DOI:10.1111/j.1467-9876.2008.00629.x (Full Text)

PMCID: PMC2975948. (Pub Med Central)

Browse | Search : All Pubs | Next