Abstract
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.
| Original language | English (US) |
|---|---|
| Journal | Statistics in Medicine |
| DOIs | |
| State | Accepted/In press - Jan 1 2019 |
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Keywords
- blinding index
- Dirichlet-multinomial
- GEE
- meta-analysis
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
Cite this
Overdispersion models for correlated multinomial data : Applications to blinding assessment. / Landsman, V.; Landsman, D.; Li, C. S.; Bang, H.
In: Statistics in Medicine, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Overdispersion models for correlated multinomial data
T2 - Applications to blinding assessment
AU - Landsman, V.
AU - Landsman, D.
AU - Li, C. S.
AU - Bang, H.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - blinding index
KW - Dirichlet-multinomial
KW - GEE
KW - meta-analysis
UR - http://www.scopus.com/inward/record.url?scp=85071317768&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071317768&partnerID=8YFLogxK
U2 - 10.1002/sim.8344
DO - 10.1002/sim.8344
M3 - Article
C2 - 31460677
AN - SCOPUS:85071317768
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
ER -