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A comparison of models for clustered binary outcomes: analysis of a designed immunology experiment

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The lymphocyte proliferative assay (LPA) of immune competence was conducted on 52 subjects, with up to 36 processing conditions per subject, to evaluate whether samples could be shipped or stored overnight, rather than being processed on fresh blood as currently required. The LPA study resulted in clustered binary data, with both cluster level and cluster-varying covariates. Two modelling strategies for the analysis of such clustered binary data are through the cluster-specific and population-averaged approaches. Whereas most research in this area has focused on the analysis of matched pairs data, in many situations, such as the LPA study, cluster sizes are naturally larger. Through considerations of interpretation and efficiency of these models when applied to large clusters, the mixed effect cluster-specific model was selected as most appropriate for the analysis of the LPA data. The model confirmed that the LPA response is significantly impaired in individuals infected with the human immunodeficiency virus (HIV). The LPA response was found to be significantly lower for shipped and overnight samples than for fresh samples, and this effect was significantly stronger among HIV-infected individuals. Surprisingly, an anticoagulant effect was not detected.

Keywords: Cluster-specific model; Conditional logistic regression; Efficiency; Generalized estimating equations; Immunologic response; Logistic regression; Lymphocyte-proliferative assay; Mixed effect; Population-averaged model

Document Type: Original Article


Affiliations: 1: Harvard School of Public Health, Boston, USA, 2: Johns Hopkins Medical School, Baltimore, USA

Publication date: January 1, 2001


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