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Drug and alcohol dependence
Jun 01, 2008;95 Suppl 1 S74-S104.

Methods for testing theory and evaluating impact in randomized field trials: intent-to-treat analyses for integrating the perspectives of person, place, and time.

C Hendricks Brown, Wei Wang, Sheppard G Kellam, Bengt O Muthén, Hanno Petras, Peter Toyinbo, Jeanne Poduska, Nicholas Ialongo, Peter A Wyman, Patricia Chamberlain, Zili Sloboda, David P MacKinnon, Amy Windham, Prevention Science and Methodology Group
Author Information
  1. C Hendricks Brown: Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B Downs Blvd., Tampa, FL 33612, United States. hbrown@health.usf.edu
PMID: 18215473 DOI: 10.1016/j.drugalcdep.2007.11.013

Abstract

Randomized field trials provide unique opportunities to examine the effectiveness of an intervention in real world settings and to test and extend both theory of etiology and theory of intervention. These trials are designed not only to test for overall intervention impact but also to examine how impact varies as a function of individual level characteristics, context, and across time. Examination of such variation in impact requires analytical methods that take into account the trial's multiple nested structure and the evolving changes in outcomes over time. The models that we describe here merge multilevel modeling with growth modeling, allowing for variation in impact to be represented through discrete mixtures--growth mixture models--and nonparametric smooth functions--generalized additive mixed models. These methods are part of an emerging class of multilevel growth mixture models, and we illustrate these with models that examine overall impact and variation in impact. In this paper, we define intent-to-treat analyses in group-randomized multilevel field trials and discuss appropriate ways to identify, examine, and test for variation in impact without inflating the Type I error rate. We describe how to make causal inferences more robust to misspecification of covariates in such analyses and how to summarize and present these interactive intervention effects clearly. Practical strategies for reducing model complexity, checking model fit, and handling missing data are discussed using six randomized field trials to show how these methods may be used across trials randomized at different levels.

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MeSH Term

Adolescent
Adult
Attention Deficit and Disruptive Behavior Disorders
Baltimore
Behavior Therapy
Child
Data Interpretation, Statistical
Humans
Longitudinal Studies
Models, Statistical
Outcome Assessment, Health Care
Randomized Controlled Trials as Topic
Research Design
Schools
Socialization
Substance-Related Disorders
Violence
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't
Article has an altmetric score of 3

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