Use of Alternating Logistic Regression in Studies of Drug-Use Clustering

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Abstract
This article describes the alternating logistic regression (ALR) method, and places this method in the context of other statistical approaches to the analysis of complex survey data, including the conditional form of logistic regression with matching on neighborhood characteristics. Unlike conditional logistic regression, the ALR method provides for an explicit estimation of the magnitude of clustering of drug use within neighborhoods and within subgroups of the neighborhood defined by male-female or age indicators, with and without covariate adjustments. The application of these ALR methods is illustrated with estimates for the magnitude of clustering of daily marijuana use and weekly marijuana use within neighborhoods of the United States, based on data from the National Household Survey on Drug Abuse samples from 1990 through 1996. [Translations are provided in the International Abstracts Section of this issue.]