Analyzing k (2 × 2) Tables Under Cluster Sampling

Abstract

Summary. In many data analytic applications, such as ophthalmologic, longitudinal, or periodontal studies, multiple observations are recorded over several sites (or timepoints) within the same subject, bringing about dependence between measurements. This correlation, in turn, precludes the use of standard statistical methods that assume independence between outcome measurements. For example, the Mantel‐Haenszel statistic, used to assess association between a binary outcome and a binary exposure while adjusting for a categorical covariate, does not follow the usual chi‐squared distribution under the null hypothesis when there is correlation between observations. A modified Mantel‐Haenszel procedure, which makes adjustment for dependence, is proposed. No particular correlation structure is assumed for responses within a cluster. This closed‐form adjustment stems from Liang and Zeger's (1986, Biometrika73, 13–22) generalized estimating equations approach for clustered data. The difference between this tabular (i.e., noniterative) technique and many earlier tabular methods is that the current method allows for consideration of site‐specific exposure and covariate information. An example from a periodontal research study illustrates application of the method.