A meta-epidemiological study to examine the association between bias and treatment effects in neonatal trials

2014 ◽  
Vol 9 (4) ◽  
pp. 1052-1059 ◽  
Author(s):  
Liza Bialy ◽  
Ben Vandermeer ◽  
Thierry Lacaze-Masmonteil ◽  
Donna M. Dryden ◽  
Lisa Hartling
Keyword(s):  
Epidemiological Study ◽  
Treatment Effects

scholarly journals 299: Small studies in meta-analyses overestimate treatment effects of aspirin for the prevention of pre-eclampsia: A systematic review and meta-epidemiological study

2018 ◽  
Vol 218 (1) ◽  
pp. S189-S190 ◽  
Author(s):  
Luis Sanchez-Ramos ◽  
Jared Roeckner ◽  
Rubymel Knupp ◽  
Andrew M. Kaunitz
Keyword(s):  
Systematic Review ◽  
Epidemiological Study ◽  
Treatment Effects ◽  
Meta Analyses

scholarly journals Registered trials report less beneficial treatment effects than unregistered ones: a meta-epidemiological study in orthodontics

2018 ◽  
Vol 100 ◽  
pp. 44-52 ◽  
Author(s):  
Spyridon N. Papageorgiou ◽  
Guilherme M. Xavier ◽  
Martyn T. Cobourne ◽  
Theodore Eliades
Keyword(s):  
Epidemiological Study ◽  
Treatment Effects

scholarly journals What is the influence of randomisation sequence generation and allocation concealment on treatment effects of physical therapy trials? A meta-epidemiological study

BMJ Open ◽  
2015 ◽  
Vol 5 (9) ◽  
pp. e008562 ◽  
Author(s):  
Susan Armijo-Olivo ◽  
Humam Saltaji ◽  
Bruno R da Costa ◽  
Jorge Fuentes ◽  
Christine Ha ◽  
...  
Keyword(s):  
Physical Therapy ◽  
Epidemiological Study ◽  
Treatment Effects ◽  
Allocation Concealment ◽  
Sequence Generation

scholarly journals Impact of blinding on estimated treatment effects in randomised clinical trials: meta-epidemiological study

BMJ ◽  
2020 ◽  
pp. l6802 ◽  
Author(s):  
Helene Moustgaard ◽  
Gemma L Clayton ◽  
Hayley E Jones ◽  
Isabelle Boutron ◽  
Lars Jørgensen ◽  
...  
Keyword(s):  
Epidemiological Study ◽  
Systematic Reviews ◽  
Treatment Effects ◽  
Healthcare Providers ◽  
Eligibility Criteria ◽  
Double Blind ◽  
Cochrane Database ◽  
Patient Reported ◽  
Meta Analyses ◽  
The Impact

Abstract Objectives To study the impact of blinding on estimated treatment effects, and their variation between trials; differentiating between blinding of patients, healthcare providers, and observers; detection bias and performance bias; and types of outcome (the MetaBLIND study). Design Meta-epidemiological study. Data source Cochrane Database of Systematic Reviews (2013-14). Eligibility criteria for selecting studies Meta-analyses with both blinded and non-blinded trials on any topic. Review methods Blinding status was retrieved from trial publications and authors, and results retrieved automatically from the Cochrane Database of Systematic Reviews. Bayesian hierarchical models estimated the average ratio of odds ratios (ROR), and estimated the increases in heterogeneity between trials, for non-blinded trials (or of unclear status) versus blinded trials. Secondary analyses adjusted for adequacy of concealment of allocation, attrition, and trial size, and explored the association between outcome subjectivity (high, moderate, low) and average bias. An ROR lower than 1 indicated exaggerated effect estimates in trials without blinding. Results The study included 142 meta-analyses (1153 trials). The ROR for lack of blinding of patients was 0.91 (95% credible interval 0.61 to 1.34) in 18 meta-analyses with patient reported outcomes, and 0.98 (0.69 to 1.39) in 14 meta-analyses with outcomes reported by blinded observers. The ROR for lack of blinding of healthcare providers was 1.01 (0.84 to 1.19) in 29 meta-analyses with healthcare provider decision outcomes (eg, readmissions), and 0.97 (0.64 to 1.45) in 13 meta-analyses with outcomes reported by blinded patients or observers. The ROR for lack of blinding of observers was 1.01 (0.86 to 1.18) in 46 meta-analyses with subjective observer reported outcomes, with no clear impact of degree of subjectivity. Information was insufficient to determine whether lack of blinding was associated with increased heterogeneity between trials. The ROR for trials not reported as double blind versus those that were double blind was 1.02 (0.90 to 1.13) in 74 meta-analyses. Conclusion No evidence was found for an average difference in estimated treatment effect between trials with and without blinded patients, healthcare providers, or outcome assessors. These results could reflect that blinding is less important than often believed or meta-epidemiological study limitations, such as residual confounding or imprecision. At this stage, replication of this study is suggested and blinding should remain a methodological safeguard in trials.

scholarly journals Assessing treatment effects and publication bias across different specialties in medicine: a meta-epidemiological study

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e045942
Author(s):  
Simon Schwab ◽  
Giuachin Kreiliger ◽  
Leonhard Held
Keyword(s):  
Epidemiological Study ◽  
Publication Bias ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Small Study ◽  
Significant Treatment ◽  
Medical Specialties ◽  
Cochrane Database ◽  
Randomised Controlled ◽  
Meta Analyses

ObjectivesTo assess the prevalence of statistically significant treatment effects, adverse events and small-study effects (when small studies report more extreme results than large studies) and publication bias (over-reporting of statistically significant results) across medical specialties.DesignLarge meta-epidemiological study of treatment effects from the Cochrane Database of Systematic Reviews.MethodsWe investigated outcomes from 57 162 studies from 1922 to 2019, and overall 98 966 meta-analyses and 5534 large meta-analyses (≥10 studies). Egger’s and Harbord’s tests to detect small-study effects, limit meta-analysis and Copas selection models to bias-adjust effect estimates and generalised linear mixed models were used to analyse one of the largest collections of evidence in medicine.ResultsMedical specialties showed differences in the prevalence of statistically significant results of efficacy and safety outcomes. Treatment effects from primary studies published in high ranking journals were more likely to be statistically significant (OR=1.52; 95% CI 1.32 to 1.75) while randomised controlled trials were less likely to report a statistically significant effect (OR=0.90; 95% CI 0.86 to 0.94). Altogether 19% (95% CI 18% to 20%) of the large meta-analyses showed evidence for small-study effects, but only 3.9% (95% CI 3.4% to 4.4%) showed evidence for publication bias after further assessment of funnel plots. Adjusting treatment effects resulted in overall less evidence for efficacy.ConclusionsThese results suggest that reporting of large treatment effects from small studies may cause greater concern than publication bias. Incentives should be created so that studies of the highest quality become more visible than studies that report more extreme results.

scholarly journals Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Geissbühler ◽  
Cesar A. Hincapié ◽  
Soheila Aghlmandi ◽  
Marcel Zwahlen ◽  
Peter Jüni ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Epidemiological Study ◽  
Treatment Effects ◽  
Aggregate Data ◽  
Ecological Fallacy ◽  
Regression Analyses ◽  
Aggregated Data ◽  
Meta Regression ◽  
Meta Regression Analysis ◽  
Meta Analyses

Abstract Background Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012. Methods A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012. Results Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls. Conclusion The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

Are biases related to attrition, missing data, and the use of intention to treat related to the magnitude of treatment effects in physical therapy trials? A meta-epidemiological study

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Susan Armijo-Olivo ◽  
Bruno R. da Costa ◽  
Christine Ha ◽  
Humam Saltaji ◽  
Greta G. Cummings ◽  
...  
Keyword(s):  
Missing Data ◽  
Physical Therapy ◽  
Epidemiological Study ◽  
Treatment Effects ◽  
Intention To Treat

Association of study design features and treatment effects in trials of chronic medical conditions: a meta-epidemiological study

2021 ◽  
pp. bmjebm-2021-111667
Author(s):  
Zhen Wang ◽  
Fares Alahdab ◽  
Magdoleen Farah ◽  
Mohamed Seisa ◽  
Mohammed Firwana ◽  
...  
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Treatment Effects ◽  
Random Sequence ◽  
Outcome Data ◽  
Allocation Concealment ◽  
Chronic Medical Conditions ◽  
Medical Conditions ◽  
Design Features ◽  
Meta Analyses

ObjectivesTo evaluate the association of study design features and treatment effects in randomised controlled trials (RCTs) evaluating therapies for individuals with chronic medical conditions.DesignMeta-epidemiological study.SettingRCTs from meta-analyses published in the 10 general medical journals with the highest impact factor published between 1 January 2007 and 10 June 2019 and evaluated a drug, procedure or device treatment of chronic medical conditions.Main outcome measuresThe association between trial design features and the effect size, reporting a ratio of ORs (ROR) and 95% confidence interval (CI).ResultsWe included 1098 trials from 86 meta-analyses. The most common outcome in the trials was mortality (52%), followed by disease progression (16%) and adverse events (12%). Lack of blinding of patients and study personnel was associated with a larger treatment effect (ROR 1.12; 95% CI 1.00 to 1.25). There was no statistically significant association with random sequence generation, allocation concealment, blinding of outcome assessors, incomplete outcome data, whether trials were stopped early, study funding, type of interventions or with type of outcomes (objective vs subjective).ConclusionThe meta-epidemiological study did not demonstrate a clear pattern of association between risk of bias indicators and treatment effects in RCTs in chronic medical conditions. The unpredictability of the direction of bias emphasises the need to make every attempt to adhere to blinding, allocation concealment and reduce attrition bias.Trial registration numberNot applicable.

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