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

PSC In The News

RSS Feed icon

Singh discusses her research in India on infertility

Johnston concerned declines in teen smoking threatened by e-cigarettes

Frey discusses book Diversity Explosion

Highlights

Apply for 2-year NICHD Postdoctoral Fellowships that begin September 2015

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Next Brown Bag

Monday, Jan 12
Filiz Garip, Changing Dynamics of Mexico-U.S. Migration

A Bayesian approach for clustered longitudinal ordinal outcome with nonignorable missing data: Evaluation of an asthma education program

Publication Abstract

Kaciroti, N.A., Trivellore Raghunathan, M.A. Schork, N.M. Clark, and M. Gong. 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, 101(474): 435-446.

Asthma, a chronic inflammatory disease of the airways, affects an estimated 6.3 million children under age 18 in the United States. A key to successful asthma management, and hence improved quality of life (QOL), calls for an active partnership between asthma patients and their health care providers. To foster this partnership, an intervention program was designed and evaluated using a randomized longitudinal study. The study focused on several outcomes where typically missing data remained a pervasive problem. We suspected that the underlying missing-data mechanism may not be ignorable. Thus here we present a method for analyzing clustered longitudinal data with missing values resulting from a nonignorable missing-data mechanism. Them transition Markov model with random effects was used to investigate changes in ordinal outcomes over time. A Bayesian pattern-mixture model with the flexibility to incorporate models for missing data in both outcome and time-varying covariates was used to model the nonignorable missing-data mechanism. The pattern-mixture model uses easy-to-understand parameters-namely, ratios of the cumulative odds across patterns with the complete-data pattern-as the reference pattern. Sensitivity analysis was performed using different prior distributions for the parameters. A fully Bayesian approach was derived by integrating over a class of prior distributions. The data from the Asthma Intervention Study were analyzed to explore the effect of the intervention program on improving QOL.

DOI:10.1198/016214505000001221 (Full Text)

Browse | Search : All Pubs | Next