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American Journal of Epidemiology Vol. 153, No. 5 : 500-505
Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health

ORIGINAL CONTRIBUTIONS

Multivariate Logistic Regression for Familial Aggregation of Two Disorders. I. Development of Models and Methods

James I. Hudson1,2,3,4, Nan M. Laird3 and Rebecca A. Betensky3

1 Biological Psychiatry Laboratory, McLean Hospital, Belmont, MA.
2 Department of Psychiatry, Harvard Medical School, Boston, MA.
3 Department of Biostatistics, Harvard School of Public Health, Boston, MA.
4 Department of Epidemiology, Harvard School of Public Health, Boston, MA.

The question of whether two disorders cluster together, or coaggregate, within families often arises. This paper considers how to analyze familial aggregation of two disorders and presents two multivariate logistic regression methods that model both disorder outcomes simultaneously. The first, a proband predictive model, predicts a relative's outcomes (the presence or absence of each of the two disorders) by using the proband's disorder status. The second, a family predictive model derived from the quadratic exponential model, predicts a family member's outcomes by using all of the remaining family members' disorder statuses. The models are more realistic, flexible, and powerful than univariate models. Methods for estimation and testing account for the correlation of outcomes among family members and can be implemented by using commercial software.

family; family characteristics; generalized estimating equation; logistic models

Abbreviations: GEE, generalized estimating equations.


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