Cutoff designs for community-based intervention studies

Authors

  • Michael L. Pennell,,

    Corresponding author
    1. Division of Biostatistics, College of Public Health, The Ohio State University, 320 West 10th Avenue, Columbus, OH 43210, U.S.A.
    • Division of Biostatistics, College of Public Health, The Ohio State University, 320 West 10th Avenue, Columbus, OH 43210, U.S.A.
    Search for more papers by this author
  • Erinn M. Hade,,

    1. Division of Biostatistics, College of Public Health, The Ohio State University, 320 West 10th Avenue, Columbus, OH 43210, U.S.A.
    2. Center for Biostatistics, The Ohio State University, U.S.A.
    Search for more papers by this author
  • David M. Murray,

    1. Division of Epidemiology, College of Public Health, The Ohio State University, U.S.A.
    Search for more papers by this author
  • Dale A. Rhoda

    1. Division of Biostatistics, College of Public Health, The Ohio State University, 320 West 10th Avenue, Columbus, OH 43210, U.S.A.
    2. Centers for Public Health Research and Evaluation, Battelle Memorial Institute, Columbus, OH, U.S.A.
    Search for more papers by this author

Errata

This article is corrected by:

  1. Errata: Correction Volume 30, Issue 21, 2669, Article first published online: 8 August 2011

Abstract

Public health interventions are often designed to target communities defined either geographically (e.g. cities, counties) or socially (e.g. schools or workplaces). The group randomized trial (GRT) is regarded as the gold standard for evaluating these interventions. However, community leaders may object to randomization as some groups may be denied a potentially beneficial intervention. Under a regression discontinuity design (RDD), individuals may be assigned to treatment based on the levels of a pretest measure, thereby allowing those most in need of the treatment to receive it. In this article, we consider analysis, power, and sample size issues in applying the RDD and related cutoff designs in community-based intervention studies. We examine the power of these designs as a function of intraclass correlation, number of groups, and number of members per group and compare results to the traditional GRT. Copyright © 2011 John Wiley & Sons, Ltd.

Ancillary