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Cited by 177Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

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

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Brown, C. Hendricks, Wang, Wei, Kellam, Sheppard G., Muthén, Bengt O., Petras, Hanno, Toyinbo, Peter, Poduska, Jeanne, Ialongo, Nicholas +5 more·2008·Drug and Alcohol Dependence
DOI 10.1016/j.drugalcdep.2007.11.013PMC PMC2560173
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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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  • A large family of efficacy and effectiveness designs now exists, including designs that randomize across persons, places, and time, and nonrandomized designs such as pre-post comparisons, regression discontinuity, time series, and multiple baseline comparisons.

    “There is now a large family of designs for testing efficacy or effectiveness that randomize across persons, place, and time (or combinations of these) (15), as well as designs that do not use randomization, such as pre-post comparisons, regression discontinuity (106), time series and multiple baseline comparisons (5).”

    From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [15]

  • Brown et al. (2008) provide guidance in how data generated from multilevel randomized designs can be analyzed to estimate heterogeneity of effects.

    “Brown et al. (2008) provide guidance in how data generated from multilevel randomized designs can be analyzed to estimate heterogeneity of effects.”

    From Standards of Evidence for Efficacy, Effectiveness, and Scale-up Research in Prevention Science: Next Generation (2015) · cites this paper as Brown et al. (2008)

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Research works printed but not linked: 8

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Authors
Brown, C. H., Wang, W., Kellam, S. G., Muthén, B. O., Petras, H., Toyinbo, P., Poduska, J., Ialongo, N., Wyman, P. A., Chamberlain, P., Sloboda, Z., MacKinnon, D. P., & Windham, A.
Affiliations
  • University of South Florida (US)
  • American Institutes for Research (US)
  • University of California, Los Angeles (US)
  • University of Maryland, College Park (US)
  • Johns Hopkins University (US)
  • University of Rochester Medicine (US)
  • Oregon Social Learning Center (US)
  • University of Akron (US)
  • Arizona State University (US)
Corresponding author
Brown, C. Hendricks
ORCID
Venue
Drug and Alcohol Dependence
Publisher
Elsevier BV
Published
2008-06
Volume
95
Pages
S74-S104
ISSN
0376-8716 (ISSN-L)
DOI
10.1016/j.drugalcdep.2007.11.013
PMC
PMC2560173
OpenAlex
W2060614575
Cited by
177
References
171
Citations by year
2026: 2 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 8 · 2021: 6 · 2020: 5 · 2019: 10 · 2018: 13 · 2017: 11 · 2016: 11 · 2015: 6 · 2014: 15 · 2013: 13 · 2012: 17
FWCI
15.75
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Drug and Alcohol Dependence (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (5t32mh018834-24)
  • National Institute on Alcohol Abuse and Alcoholism (K02AA000230, K02 AA00230)
  • National Institute of Mental Health (P30MH068685, T-32 MH018834, R01MH42968, R01MH40859, R34MH071189, R01MH076158, R01MH068423, R01MH042968, R01MH040859, P50MH38725, P50MH038725)
  • National Institute on Drug Abuse (P20DA017592, R01DA019984, R01DA015409, R01 DA01998-02S1)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
intention-to-treat analysis, group-randomized trials, generalized additive mixed models, theory testing, causal inference, type I error, nested data, missing data, model complexity
MeSH terms
Adolescent, Adult, Behavior Therapy, Child, Data Interpretation, Statistical, Humans, Longitudinal Studies, Research Design, Schools, Socialization, Violence, Baltimore, Models, Statistical, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care, Attention Deficit and Disruptive Behavior Disorders, Substance-Related Disorders

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