Likelihood methods for binary responses of present components in a cluster.
In some biomedical studies involving clustered binary responses (say, disease status), the cluster sizes can vary because some components of the cluster can be absent. When both the presence of a cluster component as well as the binary disease status of a present component are treated as responses of interest, we propose a novel two-stage random effects logistic regression framework. For the ease of interpretation of regression effects, both the marginal probability of presence/absence of a component as well as the conditional probability of disease status of a present component, preserve the approximate logistic regression forms. We present a maximum likelihood method of estimation implementable using standard statistical software. We compare our models and the physical interpretation of regression effects with competing methods from literature. We also present a simulation study to assess the robustness of our procedure to wrong specification of the random effects distribution and to compare finite-sample performances of estimates with existing methods. The methodology is illustrated via analyzing a study of the periodontal health status in a diabetic Gullah population.
Li
Xiaoyun
X
Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA. xli@stat.fsu.edu
Bandyopadhyay
Dipankar
D
Lipsitz
Stuart
S
Sinha
Debajyoti
D
D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D048909:Diabetes Complications; D004194:Disease; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D010510:Periodontal Diseases / Q000453:epidemiology; D011336:Probability
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