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

ACCESS DATA

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

Similar Datasets

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:BACKGROUND:Depression symptom questionnaires are commonly used to assess symptom severity and as screening tools to identify patients who may have depression. They are not designed to ascertain diagnostic status and, based on published sensitivity and specificity estimates, would theoretically be expected to overestimate prevalence. Meta-analyses sometimes estimate depression prevalence based on primary studies that used screening tools or rating scales rather than validated diagnostic interviews. Our objectives were to determine classification methods used in primary studies included in depression prevalence meta-analyses, if pooled prevalence differs by primary study classification methods as would be predicted, whether meta-analysis abstracts accurately describe primary study classification methods, and how meta-analyses describe prevalence estimates in abstracts. METHODS:We searched PubMed (January 2008-December 2017) for meta-analyses that reported pooled depression prevalence in the abstract. For each meta-analysis, we included up to one pooled prevalence for each of three depression classification method categories: (1) diagnostic interviews only, (2) screening or rating tools, and (3) a combination of methods. RESULTS:In 69 included meta-analyses (81 prevalence estimates), eight prevalence estimates (10%) were based on diagnostic interviews, 36 (44%) on screening or rating tools, and 37 (46%) on combinations. Prevalence was 31% based on screening or rating tools, 22% for combinations, and 17% for diagnostic interviews. Among 2094 primary studies in 81 pooled prevalence estimates, 277 (13%) used validated diagnostic interviews, 1604 (77%) used screening or rating tools, and 213 (10%) used other methods (e.g., unstructured interviews, medical records). Classification methods pooled were accurately described in meta-analysis abstracts for 17 of 81 (21%) prevalence estimates. In 73 meta-analyses based on screening or rating tools or on combined methods, 52 (71%) described the prevalence as being for "depression" or "depressive disorders." Results were similar for meta-analyses in journals with impact factor ≥ 10. CONCLUSIONS:Most meta-analyses combined estimates from studies that used screening tools or rating scales instead of diagnostic interviews, did not disclose this in abstracts, and described the prevalence as being for "depression" or "depressive disorders " even though disorders were not assessed. Users of meta-analyses of depression prevalence should be cautious when interpreting results because reported prevalence may exceed actual prevalence.

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

OmicsDI is part of the ELIXIR infrastructure

Tweets