Dataset Information

Comparison of Estimates between Cohort and Case-Control Studies in Meta-Analyses of Therapeutic Interventions: A Meta-Epidemiological Study.

ABSTRACT:

Background

Observational studies are increasingly being used for assessing therapeutic interventions. Case-control studies are generally considered to have greater risk of bias than cohort studies, but we lack evidence of differences in effect estimates between the 2 study types. We aimed to compare estimates between cohort and case-control studies in meta-analyses of observational studies of therapeutic interventions by using a meta-epidemiological study.

Methods

We used a random sample of meta-analyses of therapeutic interventions published in 2013 that included both cohort and case-control studies assessing a binary outcome. For each meta-analysis, the ratio of estimates (RE) was calculated by comparing the estimate in case-control studies to that in cohort studies. Then, we used random-effects meta-analysis to estimate a combined RE across meta-analyses. An RE < 1 indicated that case-control studies yielded larger estimates than cohort studies.

Results

The final analysis included 23 meta-analyses: 138 cohort and 133 case-control studies. Treatment effect estimates did not significantly differ between case-control and cohort studies (combined RE 0.97 [95% CI 0.86-1.09]). Heterogeneity was low, with between-meta-analysis variance ?2 = 0.0049. Estimates did not differ between case-control and prospective or retrospective cohort studies (RE = 1.05 [95% CI 0.96-1.15] and RE = 0.99 [95% CI, 0.83-1.19], respectively). Sensitivity analysis of studies reporting adjusted estimates also revealed no significant difference (RE = 1.03 [95% CI 0.91-1.16]). Heterogeneity was also low for these analyses.

Conclusion

We found no significant difference in treatment effect estimates between case-control and cohort studies assessing therapeutic interventions.

SUBMITTER: Lanza A

PROVIDER: S-EPMC4861326 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Comparison of Estimates between Cohort and Case-Control Studies in Meta-Analyses of Therapeutic Interventions: A Meta-Epidemiological Study.

Lanza Amy A Ravaud Philippe P Riveros Carolina C Dechartres Agnes A

PloS one 20160509 5

<h4>Background</h4>Observational studies are increasingly being used for assessing therapeutic interventions. Case-control studies are generally considered to have greater risk of bias than cohort studies, but we lack evidence of differences in effect estimates between the 2 study types. We aimed to compare estimates between cohort and case-control studies in meta-analyses of observational studies of therapeutic interventions by using a meta-epidemiological study.<h4>Methods</h4>We used a random ...[more]

PMID: 27159025

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Project description:BackgroundThis meta-epidemiological study aimed to assess methodological quality of a sample of contemporary non-randomised clinical studies of clinical interventions.MethodsThis was a cross-sectional study of observational studies published between January 1, 2012 and December 31, 2018. Studies were identified in PubMed using search terms 'association', 'observational,' 'non-randomised' 'comparative effectiveness' within titles or abstracts. Each study was appraised against 35 quality criteria by two authors independently, with each criterion rated fully, partially or not satisfied. These quality criteria were grouped into 6 categories: justification for observational design (n = 2); minimisation of bias in study design and data collection (n = 11); use of appropriate methods to create comparable groups (n = 6); appropriate adjustment of observed effects (n = 5); validation of observed effects (n = 9); and authors interpretations (n = 2).ResultsOf 50 unique studies, 49 (98%) were published in two US general medical journals. No study fully satisfied all applicable criteria; the mean (+/-SD) proportion of applicable criteria fully satisfied across all studies was 72% (+/- 10%). The categories of quality criteria demonstrating the lowest proportions of fully satisfied criteria were measures used to adjust observed effects (criteria 20, 23, 24) and validate observed effects (criteria 25, 27, 33). Criteria associated with ≤50% of full satisfaction across studies, where applicable, comprised: imputation methods to account for missing data (50%); justification for not performing an RCT (42%); interaction analyses in identifying independent prognostic factors potentially influencing intervention effects (42%); use of statistical correction to minimise type 1 error in multiple outcome analyses (33%); clinically significant effect sizes (30%); residual bias analyses for unmeasured or unknown confounders (14%); and falsification tests for residual confounding (8%). The proportions of fully satisfied criteria did not change over time.ConclusionsRecently published observational studies fail to fully satisfy more than one in four quality criteria. Criteria that were not or only partially satisfied were identified which serve as remediable targets for researchers and journal editors.

Project description:BackgroundTo summarize the up-to-date empirical evidence on trial-level characteristics of randomized controlled trials associated with treatment effect estimates.MethodsA systematic review searched three databases up to August 2020. Meta-epidemiological (ME) studies of randomized controlled trials on intervention effect were eligible. We assessed the methodological quality of ME studies using a self-developed criterion. Associations between treatment effect estimates and trial-level characteristics were presented using forest plots.ResultsEighty ME studies were included, with 25/80 (31%) being published after 2015. Less than one-third ME studies critically appraised the included studies (26/80, 33%), published a protocol (23/80, 29%), and provided a list of excluded studies with justifications (12/80, 15%). Trials with high or unclear (versus low) risk of bias on sequence generation (3/14 for binary outcome and 1/6 for continuous outcome), allocation concealment (11/18 and 1/6), double blinding (5/15 and 2/4) and smaller sample size (4/5 and 2/2) significantly associated with larger treatment effect estimates. Associations between high or unclear risk of bias on allocation concealment (5/6 for binary outcome and 1/3 for continuous outcome), double blinding (4/5 and 1/3) and larger treatment effect estimates were more frequently observed for subjective outcomes. The associations between treatment effect estimates and non-blinding of outcome assessors were removed in trials using multiple observers to reach consensus for both binary and continuous outcomes. Some trial characteristics in the Cochrane risk-of-bias (RoB2) tool have not been covered by the included ME studies, including using validated method for outcome measures and selection of the reported results from multiple outcome measures or multiple analysis based on results (e.g., significance of the results).ConclusionsConsistently significant associations between larger treatment effect estimates and high or unclear risk of bias on sequence generation, allocation concealment, double blinding and smaller sample size were found. The impact of allocation concealment and double blinding were more consistent for subjective outcomes. The methodological and reporting quality of included ME studies were dissatisfactory. Future ME studies should follow the corresponding reporting guideline. Specific guidelines for conducting and critically appraising ME studies are needed.

Project description:The putative risk/protective factors for several personality disorders remain unclear. The vast majority of published studies has assessed personality characteristics/traits rather than disorders. Thus, the current umbrella review of meta-analyses (MAs) aims to systematically assess risk or protective factors associated with personality disorders. We searched PubMed-MEDLINE/PsycInfo databases, up to August 31, 2020. Quality of MAs was assessed with AMSTAR-2, while the credibility of evidence for each association was assessed through standard quantitative criteria. Out of 571 initial references, five meta-analyses met inclusion criteria, encompassing 56 associations of 26 potential environmental factors for antisocial, dependent, borderline personality disorder, with a median of five studies per association, and median 214 cases per association. Overall, 35 (62.5%) of the associations were nominally significant. Six associations met class II (i.e., highly suggestive) evidence for borderline personality disorder, with large effect sizes involving childhood emotional abuse (OR = 28.15, 95% CI 14.76-53.68), childhood emotional neglect (OR = 22.86, 95% CI 11.55-45.22), childhood any adversities (OR = 14.32, 95% CI 10.80-18.98), childhood physical abuse (OR = 9.30, 95% CI 6.57-13.17), childhood sexual abuse (OR = 7.95, 95% CI 6.21-10.17), and childhood physical neglect (OR = 5.73, 95% CI 3.21-10.21), plus 16 further associations supported by class IV evidence. No risk factor for antisocial or dependent personality disorder was supported by class I, II, and III, but six and seven met class IV evidence, respectively. Quality of included meta-analyses was rated as moderate in two, critically low in three. The large effect sizes found for a broad range of childhood adversities suggest that prevention of personality disorders should target childhood-related risk factors. However, larger cohort studies assessing multidimensional risk factors are needed in the field.

Project description:ObjectiveTo assess the influence of trial sample size on treatment effect estimates within meta-analyses.DesignMeta-epidemiological study.Data sources93 meta-analyses (735 randomised controlled trials) assessing therapeutic interventions with binary outcomes, published in the 10 leading journals of each medical subject category of the Journal Citation Reports or in the Cochrane Database of Systematic Reviews.Data extractionSample size, outcome data, and risk of bias extracted from each trial.Data synthesisTrials within each meta-analysis were sorted by their sample size: using quarters within each meta-analysis (from quarter 1 with 25% of the smallest trials, to quarter 4 with 25% of the largest trials), and using size groups across meta-analyses (ranging from <50 to ≥ 1000 patients). Treatment effects were compared within each meta-analysis between quarters or between size groups by average ratios of odds ratios (where a ratio of odds ratios less than 1 indicates larger effects in smaller trials).ResultsTreatment effect estimates were significantly larger in smaller trials, regardless of sample size. Compared with quarter 4 (which included the largest trials), treatment effects were, on average, 32% larger in trials in quarter 1 (which included the smallest trials; ratio of odds ratios 0.68, 95% confidence interval 0.57 to 0.82), 17% larger in trials in quarter 2 (0.83, 0.75 to 0.91), and 12% larger in trials in quarter 3 (0.88, 0.82 to 0.95). Similar results were obtained when comparing treatment effect estimates between different size groups. Compared with trials of 1000 patients or more, treatment effects were, on average, 48% larger in trials with fewer than 50 patients (0.52, 0.41 to 0.66) and 10% larger in trials with 500-999 patients (0.90, 0.82 to 1.00).ConclusionsTreatment effect estimates differed within meta-analyses solely based on trial sample size, with stronger effect estimates seen in small to moderately sized trials than in the largest trials.

Dataset Information

Comparison of Estimates between Cohort and Case-Control Studies in Meta-Analyses of Therapeutic Interventions: A Meta-Epidemiological Study.

Background

Methods

Results

Conclusion

Publications

Comparison of Estimates between Cohort and Case-Control Studies in Meta-Analyses of Therapeutic Interventions: A Meta-Epidemiological Study.

Similar Datasets

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