Biomedical subjects

Sherri P Varnell

Publications and source records attributed to Sherri P Varnell.

4 recordsLinked to original sources

PubMed2004

Assessing the most powerful analysis method for school-based intervention studies with alcohol, tobacco, and other drug outcomes.

This article compares four mixed-model analyses valid for group-randomized trials (GRTs) involving a nested cohort design with a single pretest and a single posttest, the most common design used in GRTs. This study makes estimates of intraclass correlations (ICCs) available to investigators planning GRTs with alcohol, tobacco, and other drug measures as the outcomes of interest. It also provides formulae demonstrating the potential benefits to the standard error of the intervention effect of both adjustments for fixed and time-varying covariates, as well as correlations over time. These estimates will allow other researchers using these variables to plan their studies by performing a priori power analyses for any of four common analytic options.

Adolescent↗

PubMed2004

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

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 (sigma(delta)) 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.

Adolescent↗

PubMed2004

Design and analysis of group-randomized trials: a review of recent practices.

We reviewed group-randomized trials (GRTs) published in the American Journal of Public Health and Preventive Medicine from 1998 through 2002 and estimated the proportion of GRTs that employ appropriate methods for design and analysis. Of 60 articles, 9 (15.0%) reported evidence of using appropriate methods for sample size estimation. Of 59 articles in the analytic review, 27 (45.8%) reported at least 1 inappropriate analysis and 12 (20.3%) reported only inappropriate analyses. Nineteen (32.2%) reported analyses at an individual or subgroup level, ignoring group, or included group as a fixed effect. Hence increased vigilance is needed to ensure that appropriate methods for GRTs are employed and that results based on inappropriate methods are not published.

Bibliometrics↗

PubMed2004

Design and analysis of group-randomized trials: a review of recent methodological developments.

We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials.

Cluster Analysis↗