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Cited by 144Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Effect sizes and statistical testing in the determination of clinical significance in behavioral medicine research

Rutledge, Thomas, Loh, Cathy·2004·Annals of Behavioral Medicine
DOI 10.1207/s15324796abm2702_9
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Abstract

BACKGROUND: The interpretation of clinical significance continues to be an obstacle for researchers in behavioral medicine. PURPOSE: To review selected behavioral medicine research to critically examine the perception among investigators that behavioral effects on health are small based on common metrics of clinical significance. METHODS: Using quantitative findings from recent behavioral medicine research in medical and psychiatric journals, we explored results in terms of several statistical metrics to assess potential clinical significance: r coefficients, risk ratios, risk difference measures, and attributable risk. RESULTS: Translated into r coefficients, even established health predictors such as smoking, obesity, and fitness had only modest effects (rs =.03-.22), and the range of effect sizes were comparable with those based on psychological predictors including depression and stress-reactivity (rs =.06-.22). In contrast, effects for both classes of predictors were suggestive of clinical significance based on public health statistics. CONCLUSIONS: Our choice of statistics for defining "small" and "large" effect sizes affects the perceived importance of behavioral health findings. In the assessment of health outcomes with low incidence rates, effects expressed as correlations using even the most robust predictors will often appear small. In these instances, we challenge researchers to move beyond conventional data analysis approaches and to expand their clinical interpretation efforts by employing additional statistical methods favored in medicine and public health.

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  • There is ample room for improvement regarding the understanding of abstract statistical concepts by medical students and medical professionals.

    “Several studies suggest that there is ample room for improvement regarding the understanding of abstract statistical concepts by medical students and medical professionals (Hoffrage et al., 2000; Rutledge et al., 2004).”

    From Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error (2023) · cites this paper as Rutledge et al., 2004

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Authors
Rutledge, T., & Loh, C.
Affiliations
University of California San Diego (US)
ORCID
Rutledge, Thomas
Venue
Annals of Behavioral Medicine
Publisher
Oxford University Press (OUP)
Published
2004-04
Volume
27
Issue
2
Pages
138-145
ISSN
  • 0742-5554 (ISSN-L)
  • 0883-6612
  • 1532-4796
DOI
10.1207/s15324796abm2702_9
OpenAlex
W2160654074
Cited by
144
References
53
Citations by year
2026: 4 · 2025: 5 · 2024: 4 · 2023: 3 · 2022: 4 · 2021: 29 · 2020: 1 · 2019: 7 · 2018: 6 · 2017: 7 · 2016: 7 · 2015: 7 · 2014: 9 · 2013: 2 · 2012: 6
FWCI
1.07
Type
journal article
Language
en
Open access
closed
Locations
  • Annals of Behavioral Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
clinical significance, effect size, behavioral medicine, relative risk, risk difference, attributable risk, smoking, obesity, physical fitness, depression, stress reactivity, statistical interpretation
MeSH terms
Behavioral Medicine, Data Interpretation, Statistical, Humans, Research Design, Research Personnel, Sensitivity and Specificity, Models, Statistical, Behavioral Research

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