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Comparison of Evidence of Treatment Effects in Randomized and Nonrandomized Studies
Abstract
CONTEXT: There is substantial debate about whether the results of nonrandomized studies are consistent with the results of randomized controlled trials on the same topic. OBJECTIVES: To compare results of randomized and nonrandomized studies that evaluated medical interventions and to examine characteristics that may explain discrepancies between randomized and nonrandomized studies. DATA SOURCES: MEDLINE (1966-March 2000), the Cochrane Library (Issue 3, 2000), and major journals were searched. STUDY SELECTION: Forty-five diverse topics were identified for which both randomized trials (n = 240) and nonrandomized studies (n = 168) had been performed and had been considered in meta-analyses of binary outcomes. DATA EXTRACTION: Data on events per patient in each study arm and design and characteristics of each study considered in each meta-analysis were extracted and synthesized separately for randomized and nonrandomized studies. DATA SYNTHESIS: Very good correlation was observed between the summary odds ratios of randomized and nonrandomized studies (r = 0.75; P<.001); however, nonrandomized studies tended to show larger treatment effects (28 vs 11; P =.009). Between-study heterogeneity was frequent among randomized trials alone (23%) and very frequent among nonrandomized studies alone (41%). The summary results of the 2 types of designs differed beyond chance in 7 cases (16%). Discrepancies beyond chance were less common when only prospective studies were considered (8%). Occasional differences in sample size and timing of publication were also noted between discrepant randomized and nonrandomized studies. In 28 cases (62%), the natural logarithm of the odds ratio differed by at least 50%, and in 15 cases (33%), the odds ratio varied at least 2-fold between nonrandomized studies and randomized trials. CONCLUSIONS: Despite good correlation between randomized trials and nonrandomized studies-in particular, prospective studies-discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common.
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“The healthy subject effect is a particularly pernicious form of bias in which subjects with generally healthier lifestyles adopt a particular treatment in a nonrandomized study, resulting in unobserved confounding [268].”
From Personality and Longevity: Knowns, Unknowns, and Implications for Public Health and Personalized Medicine (2011) · cites this paper as [268]
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Metadata
- Authors
- Ioannidis, J. P. A.
- Affiliations
- Tufts Medical Center (US)
- Corresponding author
- Ioannidis, John P. A.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2001-08-15
- Volume
- 286
- Issue
- 7
- Pages
- 821
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2084917404
- Cited by
- 919
- References
- 50
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 39 · 2023: 24 · 2022: 46 · 2021: 59 · 2020: 53 · 2019: 39 · 2018: 31 · 2017: 41 · 2016: 39 · 2015: 40 · 2014: 42 · 2013: 45 · 2012: 39
- FWCI
- 58.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- randomized controlled trials, non-randomized studies, observational studies, treatment effect estimates, odds ratio, study heterogeneity, prospective study, medical interventions, publication timing
- MeSH terms
- Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Meta-Analysis as Topic, Randomized Controlled Trials as Topic, Evidence-Based Medicine
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