Assessing intervention effects in a school-based nutrition intervention trial: Which analytic model is most powerful?

Jessica B. Janega, David M. Murray, Sherri P. Varnell, Jonathan L. Blitstein, Amanda S. Birnbaum, Leslie A. Lytle

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

This article compares four mixed-model analyses valid for group-randomized trials (GRTs) involving a nested cohort design with a single pretest and posttest. This study makes estimates of intraclass correlations (ICCs) available to investigators planning GRTs addressing dietary outcomes. It also provides formulae demonstrating the potential benefits to the standard error of the intervention effect (σ Δ) from adjustments for both fixed and time-varying covariates and correlations over time. These estimates will allow other researchers using these variables to plan their studies by estimating a priori detectable differences and sample size requirements for any of the four analytic options. These methods are demonstrated using data from the Teens Eating for Energy and Nutrition at School study. Mixed-model analyses of covariance proved to be the most powerful analysis in that data set. The formulae may be applied to any dependent variable in any GRT given corresponding information for those variables on the parameters that define the formulae.

Original languageEnglish
Pages (from-to)756-774
Number of pages19
JournalHealth Education and Behavior
Volume31
Issue number6
DOIs
StatePublished - 1 Dec 2004

Keywords

  • Analytic methods
  • Group-randomized trial
  • Power

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