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The effects of excluding patients from the analysis in randomised controlled trials: meta-epidemiological study
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OBJECTIVE: To examine whether excluding patients from the analysis of randomised trials are associated with biased estimates of treatment effects and higher heterogeneity between trials. DESIGN: Meta-epidemiological study based on a collection of meta-analyses of randomised trials. DATA SOURCES: 14 meta-analyses including 167 trials that compared therapeutic interventions with placebo or non-intervention control in patients with osteoarthritis of the hip or knee and used patient reported pain as an outcome. METHODS: Effect sizes were calculated from differences in means of pain intensity between groups at the end of follow-up, divided by the pooled standard deviation. Trials were combined by using random effects meta-analysis. Estimates of treatment effects were compared between trials with and trials without exclusions from the analysis, and the impact of restricting meta-analyses to trials without exclusions was assessed. RESULTS: 39 trials (23%) had included all patients in the analysis. In 128 trials (77%) some patients were excluded from the analysis. Effect sizes from trials with exclusions tended to be more beneficial than those from trials without exclusions (difference -0.13, 95% confidence interval -0.29 to 0.04). However, estimates of bias between individual meta-analyses varied considerably (tau(2)=0.07). Tests of interaction between exclusions from the analysis and estimates of treatment effects were positive in five meta-analyses. Stratified analyses indicated that differences in effect sizes between trials with and trials without exclusions were more pronounced in meta-analyses with high between trial heterogeneity, in meta-analyses with large estimated treatment benefits, and in meta-analyses of complementary medicine. Restriction of meta-analyses to trials without exclusions resulted in smaller estimated treatment benefits, larger P values, and considerable decreases in between trial heterogeneity. CONCLUSION: Excluding patients from the analysis in randomised trials often results in biased estimates of treatment effects, but the extent and direction of bias is unpredictable. Results from intention to treat analyses should always be described in reports of randomised trials. In systematic reviews, the influence of exclusions from the analysis on estimated treatment effects should routinely be assessed.
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“Effect sizes from trials that excluded participants in their analysis or that used a modified ITT protocol tended to be more beneficial than those from trials without exclusions, demonstrating that the ITT principle is important to preserve the benefits of randomization and keep unbiased estimates when the objective of the trial is to investigate effectiveness.110”
From Effectiveness of Manual Therapy and Therapeutic Exercise for Temporomandibular Disorders: Systematic Review and Meta-Analysis (2016) · cites this paper as [110]
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In the citing paper
“We excluded studies with less than 20 participants at the end of treatment, because of the recognised risk of bias of small numbers (Ioannidis 2005; Nuesch 2009).”
From Psychological therapies for the management of chronic pain (excluding headache) in adults (2020) · cites this paper as Nuesch 2009
Experimental — accuracy not yet measuredIn the citing paper
“exclusion of participants from RCT analyses was shown to distort outcomes42,43”
From The effectiveness of virtual reality based interventions for symptoms of anxiety and depression: A meta-analysis (2018) · cites this paper as [43]
In the citing paper
“An intention-to-treat analysis population is defined by the inclusion of outcome data from all participants in their originally allocated group—this is widely recommended as the preferred analysis population.360 419 420”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [360]
In the citing paper
“Trials at high or unclear risk of bias tend to overestimate benefits and underestimate harms.”
From Should antidepressants be used for major depressive disorder? (2020) · cites this paper as [40]
In the citing paper
“This last criterion of N > 20 at post-treatment assessment is an improvement from the Eccleston 2009a review in which we used an entry of N > 10.We made this change because of the recognised risk of bias of small numbers (Ioannidis 2005;Nuesch 2009); raising the required N further would be desirable but would exclude too many studies.”
From Psychological therapies for the management of chronic pain (excluding headache) in adults (2012) · cites this paper as Nuesch 2009
In the citing paper
“the overall tendency for poorer quality trials to produce more positive results (Furlan 2001;Ioannidis 2005;Nuesch 2009).”
From Psychological therapies for the management of chronic pain (excluding headache) in adults (2012) · cites this paper as Nuesch 2009
In the citing paper
“given the demonstrated association between small numbers and bias (Ioannidis 2005; Moore 2010; Nuesch 2009).”
From Psychological therapies for the management of chronic pain (excluding headache) in adults (2012) · cites this paper as Nuesch 2009
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“This decision was made based on previous reports that different forms of bias can have a major impact on the estimates of treatment benefits in osteoarthritis trials [39–41].”
Results of Database Studies in Spine Surgery Can Be Influenced by Missing Data
Basques, B. A., McLynn, R. P., Fice, M. P., Samuel, A. M., Lukasiewicz, A. M., Bohl, D. D., Ahn, J. J., Singh, K., & Grauer, J. N. (2016) · Clinical Orthopaedics and Related Research · doi:10.1007/s11999-016-5175-7 · Cited by 70
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“This decision was to minimise potential risk of bias associatedwith trials of small sample size and to address the overall tendency for smaller or poor-quality trials to produce more positive results (Ioannidis, 2005; Furlan et al., 2001; Nüesch et al., 2009).”
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“This is important because excluding patients from the analysis in a randomized trial may bias the estimate of the treatment effect in an unpredictable way.(25) This study also had limitations.”
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BackgroundResultInfluentialModified intention-to-treat analysis did not bias trial results
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Metadata
- Authors
- Nuesch, E., Trelle, S., Reichenbach, S., Rutjes, A. W. S., Burgi, E., Scherer, M., Altman, D. G., & Juni, P.
- Affiliations
- University of Bern (CH)
- University of Göttingen (DE)
- University of Oxford (GB)
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2009-09-07
- Volume
- 339
- Issue
- sep07 1
- Pages
- b3244-b3244
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- PMC
- PMC2739282
- OpenAlex
- W2057810715
- Cited by
- 248
- References
- 52
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 8 · 2023: 9 · 2022: 7 · 2021: 15 · 2020: 20 · 2019: 11 · 2018: 11 · 2017: 15 · 2016: 15 · 2015: 17 · 2014: 17 · 2013: 20 · 2012: 22
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- BMJ (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by-nc)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by-nc)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- Open Access CRIS of the University of Bern
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (115067, 104762, 66377, 066377, 66378)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Nursing › Issues, ethics and legal aspects › Pharmaceutical industry and healthcare
- Keywords
- randomized controlled trials, bias in treatment effect estimates, between-trial heterogeneity, intention-to-treat analysis, osteoarthritis, hip osteoarthritis, knee osteoarthritis, patient-reported pain, complementary therapy, meta-epidemiology
- MeSH terms
- Data Collection, Humans, Pain, Meta-Analysis as Topic, Osteoarthritis, Hip, Bias, Randomized Controlled Trials as Topic, Sample Size, Osteoarthritis, Knee
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