Overdispersion models for correlated multinomial data: Applications to blinding assessment

Overdispersion models have been extensively studied for correlated normal and binomial data but much less so for correlated multinomial data. In this work, we describe a multinomial overdispersion model that leads to the specification of the first two moments of the outcome and allows the estimation...

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Bibliographic Details
Published in:Statistics in medicine 2019-11, Vol.38 (25), p.4963-4976
Main Authors: Landsman, V., Landsman, D., Li, C.S., Bang, H.
Format: Article
Language:English
Subjects:
Biometry
blinding index
Cluster Analysis
Computer Simulation
Dirichlet‐multinomial
GEE
Humans
Likelihood Functions
Medical research
Mental Disorders - therapy
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Neck Pain - therapy
Randomized Controlled Trials as Topic - statistics & numerical data
Research Design
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Summary:Overdispersion models have been extensively studied for correlated normal and binomial data but much less so for correlated multinomial data. In this work, we describe a multinomial overdispersion model that leads to the specification of the first two moments of the outcome and allows the estimation of the global parameters using generalized estimating equations (GEE). We introduce a Global Blinding Index as a target parameter and illustrate the application of the GEE method to its estimation from (1) a clinical trial with clustering by practitioner and (2) a meta‐analysis on psychiatric disorders. We examine the impact of a small number of clusters, high variability in cluster sizes, and the magnitude of the intraclass correlation on the performance of the GEE estimators of the Global Blinding Index using the data simulated from different models. We compare these estimators with the inverse‐variance weighted estimators and a maximum‐likelihood estimator, derived under the Dirichlet‐multinomial model. Our results indicate that the performance of the GEE estimators was satisfactory even in situations with a small number of clusters, whereas the inverse‐variance weighted estimators performed poorly, especially for larger values of the intraclass correlation coefficient. Our findings and illustrations may be instrumental for practitioners who analyze clustered multinomial data from clinical trials and/or meta‐analysis.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.8344