z-logo
Premium
Overdispersion models for correlated multinomial data: Applications to blinding assessment
Author(s) -
Landsman V.,
Landsman D.,
Li C.S.,
Bang H.
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8344
Subject(s) - overdispersion , statistics , estimator , generalized estimating equation , multinomial distribution , intraclass correlation , mathematics , gee , econometrics , estimating equations , negative binomial distribution , quasi likelihood , poisson distribution , psychometrics
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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here