The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.
Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis
Open accessAbstract
The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the intervention and control groups that captures the treatment effect is rarely reported. This article first reviews 2 classes of formulas for effect sizes associated with classical repeated-measures designs that use the standard deviation of either change scores or raw scores for the denominator. It then broadens the scope to subsume GMA and demonstrates that the independent groups, within-subjects, pretest-posttest control-group, and GMA designs all estimate the same effect size when the standard deviation of raw scores is uniformly used. Finally, the article shows that the correct effect size for treatment efficacy in GMA--the difference between the estimated means of the 2 groups at end of study (determined from the coefficient for the slope difference and length of study) divided by the baseline standard deviation--is not reported in clinical trials.
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“Effect sizes for each longitudinal analysis were computed using the formula provided by Feingold (2009) and interpreted using Cohen (1988)’s specifications.”
From Impact of dialectical behavior therapy versus community treatment by experts on emotional experience, expression, and acceptance in borderline personality disorder (2014) · cites this paper as Feingold, 2009
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“LGCM is accepted as a suitable framework for use in the evaluation of efficacy in psychological interventions [22]”
From Large-scale dissemination of internet-based cognitive behavioral therapy for youth anxiety: Feasibility and acceptability study (2018) · cites this paper as [22]
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“LGCM is accepted as a suitable framework for use in the evaluation of efficacy in psychological interventions [22] and allows determination of whether the temporal trajectory from the baseline to Session 10 is significant, using data from all participants rather than just the treatment completers [22].”
From Large-scale dissemination of internet-based cognitive behavioral therapy for youth anxiety: Feasibility and acceptability study (2018) · cites this paper as [22]
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2 of the 58 references couldn’t be matched to a known paper.
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- Becker BJ Synthesizing standardized mean-change scores. British Journal of Mathematical and Statistical Psychology 1988 41 257 278
- Gibbons RD Hedeker DR Davis JM Estimation of effect sizes from a series of experiments involving paired comparisons. Journal of Educational Statistics 1993 18 271 279
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Stice, E., Marti, C. N., & Rohde, P. (2013) · Journal of Abnormal Psychology · doi:10.1037/a0030679 · Cited by 822
Bamelis, L. L. M., Evers, S. M. A. A., Spinhoven, P., & Arntz, A. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2013.12040518 · Cited by 497
Arch, J. J., Eifert, G. H., Davies, C., Vilardaga, J. C. P., Rose, R. D., & Craske, M. G. (2012) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0028310 · Cited by 443
Twohig, M. P., Hayes, S. C., Plumb, J. C., Pruitt, L. D., Collins, A. B., Hazlett-Stevens, H., & Woidneck, M. R. (2010) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0020508 · Cited by 434
“Effect sizes for MMRM outcomes were calculated as specified by Wackerly, Mendenhall, and Scheaffer (2008) and for HLM by examining end-point outcomes as suggested by Feingold (2009).”
MethodNeacsiu, A. D., Eberle, J. W., Kramer, R., Wiesmann, T., & Linehan, M. M. (2014) · Behaviour Research and Therapy · doi:10.1016/j.brat.2014.05.005 · Cited by 394
Metadata
- Authors
- Feingold, A.
- Affiliations
- Oregon Social Learning Center (US)
- Corresponding author
- Feingold, Alan
- Venue
- Psychological Methods
- Published
- 2009
- Volume
- 14
- Issue
- 1
- Pages
- 43-53
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- PMC
- PMC2712654
- OpenAlex
- W2089811587
- Cited by
- 851
- References
- 58
- Citations by year
- 2026: 56 · 2025: 60 · 2024: 61 · 2023: 54 · 2022: 56 · 2021: 72 · 2020: 61 · 2019: 47 · 2018: 56 · 2017: 51 · 2016: 65 · 2015: 65 · 2014: 63 · 2013: 35 · 2012: 24
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD046364)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- effect size, growth models, latent growth curve models, multilevel analysis, repeated measures designs, change scores, treatment effects, intervention efficacy, pretest-posttest control group design
- MeSH terms
- Humans, Psychology, Social, Research Design, Meta-Analysis as Topic, Reproducibility of Results, Models, Statistical, Linear Models, Treatment Outcome, Controlled Clinical Trials as Topic
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