The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.
Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333
Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075823 · Cited by 18,773
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Hierarchical Linear Models: Applications and Data Analysis Methods
Davidian, M. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s288 · Cited by 13,820
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510701575396 · Cited by 11,415
Self-citation
Research works printed but not linked: 2
- Muthén, B.O. Latent variable hybrids: overview of old and new models. In: Hancock, G.R., Samuelsen, K.M. (Eds.), Advances in Latent Variable Mixture Models. Information Age Publishing, Inc., Charlotte, NC, in press.
- Wyman, P.A., Brown, C.H., Inman, J., Cross, W., Schmeelk-Cone, K., Guo, J., Peña, J. Randomized trial of a gatekeeper training program for suicide prevention. J Clin. Consult. Psychol., in press.
Cited by
Sorted by citations
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-013-0528-y · Cited by 12,038
“Another alternative to the classic RCT are the adaptive designs proposed by Brown et al. (2006, 2008, 2009).”
MethodGroup-Based Trajectory Modeling in Clinical Research
Nagin, D. S., & Odgers, C. L. (2010) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.121208.131413 · Cited by 2,749
(2009) · Cited by 2,013
Gottfredson, D. C., Cook, T. D., Gardner, F. E. M., Gorman-Smith, D., Howe, G. W., Sandler, I. N., & Zafft, K. M. (2015) · Prevention Science · doi:10.1007/s11121-015-0555-x · Cited by 627
Wyman, P. A., Brown, C. H., LoMurray, M., Schmeelk-Cone, K., Petrova, M., Yu, Q., Walsh, E., Tu, X., & Wang, W. (2010) · American Journal of Public Health · doi:10.2105/ajph.2009.190025 · Cited by 536
Self-citation
Metadata
- 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)
- 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
- green
- europepmc.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
