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Regression to the mean and alcohol consumption: A cohort study exploring implications for the interpretation of change in control groups in brief intervention trials
Open accessAbstract
BACKGROUND: Reductions in drinking among individuals randomised to control groups in brief alcohol intervention trials are common and suggest that asking study participants about their drinking may itself cause them to reduce their consumption. We sought to test the hypothesis that the statistical artefact regression to the mean (RTM) explains part of the reduction in such studies. METHODS: 967 participants in a cohort study of alcohol consumption in New Zealand provided data at baseline and again six months later. We use graphical methods and apply thresholds of 8, 12, 16 and 20 in AUDIT scores to explore RTM. RESULTS: There was a negative association between baseline AUDIT scores and change in AUDIT scores from baseline to six months, which in the absence of bias and confounding, is RTM. Students with lower baseline scores tended to have higher follow-up scores and conversely, those with higher baseline scores tended to have lower follow-up scores. When a threshold score of 8 was used to select a subgroup, the observed mean change was approximately half of that observed without a threshold. The application of higher thresholds produced greater apparent reductions in alcohol consumption. CONCLUSIONS: Part of the reduction seen in the control groups of brief alcohol intervention trials is likely to be due to RTM and the amount of change is likely to be greater as the threshold for entry to the trial increases. Quantification of RTM warrants further study and should assist understanding assessment and other research participation effects.
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Reduction of alcohol use regardless of treatment allocation is observed in other alcohol intervention trials, especially in emergency care settings, due to contextual factors such as trauma exposure or the acute care experience.
“This is observed in other alcohol intervention trials, especially those conducted in emergency care settings, due to contextual factors such as trauma exposure or the acute care experience [15,27,37,41–43].”
From Effectiveness of a brief intervention and text-based booster in the emergency department to reduce harmful and hazardous alcohol use: A pragmatic randomized adaptive clinical trial in Moshi, Tanzania (2025) · cites this paper as [42]
The Introduction and Discussion (page numbers unavailable) report reduced drinking in control groups of brief alcohol intervention trials, supporting the observation of reductions without the intervention. They do not identify emergency care trials as an especially prominent setting or attribute reductions to trauma exposure or the acute care experience; the paper instead discusses regression to the mean and possible research-participation effects. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe impact of online self-help in reducing alcohol consumption may have been underestimated because control group drinking was also assessed, and such assessment alone possibly motivates controls to reduce their consumption.
“Third, the impact of online self-help in reducing alcohol consumption may have been underestimated here because control group drinking was also assessed; such assessment alone possibly motivates controls to reduce their consumption [83], [84].”
From Effectiveness of Guided and Unguided Low-Intensity Internet Interventions for Adult Alcohol Misuse: A Meta-Analysis (2014) · cites this paper as [84]
The impact of online self-help in reducing alcohol consumption may have been underestimated because control group drinking was also assessed, and such assessment alone possibly motivates controls to reduce their consumption.
“Third, the impact of online self-help in reducing alcohol consumption may have been underestimated here because control group drinking was also assessed; such assessment alone possibly motivates controls to reduce their consumption [83], [84].”
From Effectiveness of Guided and Unguided Low-Intensity Internet Interventions for Adult Alcohol Misuse: A Meta-Analysis (2014) · cites this paper as [83]
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Metadata
- Authors
- McCambridge, J., Kypri, K., & McElduff, P.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- University of Newcastle Australia (AU)
- Corresponding author
- McCambridge, Jim
- Venue
- Drug and Alcohol Dependence
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 135
- Pages
- 156-159
- ISSN
- 0376-8716 (ISSN-L)
- PMC
- PMC3929002
- OpenAlex
- W2104367365
- Cited by
- 54
- References
- 41
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 2 · 2022: 11 · 2021: 5 · 2020: 4 · 2019: 1 · 2018: 7 · 2017: 3 · 2016: 5 · 2015: 5 · 2014: 3
- FWCI
- 3.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Drug and Alcohol Dependence (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Figshare (submittedVersion)
- Funders
- Wellcome Trust (WT086516MA)
- Topics
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Health Sciences › Medicine › Pathology and Forensic Medicine › Alcohol Consumption and Health Effects
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- regression to the mean, alcohol consumption, brief alcohol intervention, control groups, trial interpretation, AUDIT score, threshold selection, students
- MeSH terms
- Adolescent, Alcohol Drinking, Female, Humans, Longitudinal Studies, Male, New Zealand, Surveys and Questionnaires, Regression Analysis, Students, Cohort Studies, Control Groups, Young Adult, Early Medical Intervention
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 ›
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