Person-time analysis of paired community intervention trials when the number of communities is small

The objective of this paper is to evaluate person-time methods of analysis of paired community intervention trials when the number of community pairs is small and considers methods to account for individual level covariates.

Abstract

Community intervention trials involve randomization of communities to either an intervention or control arm. The objective of this paper is to evaluate person-time methods of analysis of paired community intervention trials when the number of community pairs is small. We consider several test procedures and evaluate their performance by simulation. Naive methods that ignore intracluster correlation, such as standard Mantel-Haenszel type statistics, can be misleading. The performance of the paired t-test depends on the distribution of the random community effects. Permutation tests perform well for the ranges of situations considered. However, there can be considerable loss of power with permutation methods compared to standard Mantel-Haenszel methods if in fact there is no intracluster correlation when the number of pairs is small. We consider methods to account for individual level covariates. Motivation for this work came from recent randomized community intervention trials in Africa to prevent transmission of the human immunodeficiency virus (HIV).