Logistic regression for clustered binary data in proband studies with application to familial aggregation of sleep disorders.
Estimation is considered for the class of conditional logistic regression models for clustered binary data proposed by Qu et al. (Communications in Statistics, Series A 16, 3447-3476, 1987) when clusters are sampled on the basis of the outcome for one or more cluster members. The problem is suggested by data from a study designed to investigate familial aggregation of sleep disorders. After appropriate consideration of the mode of ascertainment of "cases" and "controls," it is shown that the model is preserved under this form of sampling, and a method of estimation is presented. The inconsistency of two alternative methods is demonstrated, and an example is provided.
Tosteson
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Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Rosner
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B
Redline
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D001699:Biometry; D016000:Cluster Analysis; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D012044:Regression Analysis; D012891:Sleep Apnea Syndromes / Q000235:genetics