Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

S. Mukhopadhyay, Stephen Warwick Looney

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.

Original languageEnglish (US)
Pages (from-to)1293-1305
Number of pages13
JournalJournal of Applied Statistics
Volume36
Issue number11
DOIs
StatePublished - Nov 1 2009

Fingerprint

Quantile
Intracluster Correlation
Graph in graph theory
Effect Size
Randomized Trial
Power Function
Prior Knowledge
Design
Graph
Unknown
Necessary
Methodology

Keywords

  • Effect size
  • Intracluster correlation
  • Noncentrality parameter
  • Power function
  • Quantile dispersion graphs

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs. / Mukhopadhyay, S.; Looney, Stephen Warwick.

In: Journal of Applied Statistics, Vol. 36, No. 11, 01.11.2009, p. 1293-1305.

Research output: Contribution to journalArticle

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