Adjusted chi-square statistics: Application to clustered binary data in primary care

Abstract

The frequency of randomized cluster trials is increasing in primary care research. These trials are differentiated by the randomization method, in which a group of individuals is randomly assigned to an intervention as a cluster rather than as individuals. Characteristically, individuals within a cluster tend to be more alike than individuals selected at random. For instance, evaluating the effect of an intervention across medical care providers at an institutional level or at a physician group practice level fits the randomized cluster model. Three examples in this article show how failure to account for the dependence introduced by unit of randomization can affect the analysis of binary data and the conclusions of randomized cluster trials. Greater consideration of the nested nature of patient, physician, and practice data would increase the quality of primary care research.