Prognostic eff ect size of cardiovascular biomarkers in datasets from observational studies versus randomised trials: Meta-epidemiology study

Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
BMJ (online) (Impact Factor: 17.45). 11/2011; 343(nov07 1):d6829. DOI: 10.1136/bmj.d6829
Source: PubMed


To compare the reported effect sizes of cardiovascular biomarkers in datasets from observational studies with those in datasets from randomised controlled trials.
Review of meta-analyses.
Meta-analyses of emerging cardiovascular biomarkers (not part of the Framingham risk score) that included datasets from at least one observational study and at least one randomised controlled trial were identified through Medline (last update, January 2011).
Study-specific risk ratios were extracted from all identified meta-analyses and synthesised with random effects for (a) all studies, and (b) separately for observational and for randomised controlled trial populations for comparison.
31 eligible meta-analyses were identified. For seven major biomarkers (C reactive protein, non-HDL cholesterol, lipoprotein(a), post-load glucose, fibrinogen, B-type natriuretic peptide, and troponins), the prognostic effect was significantly stronger in datasets from observational studies than in datasets from randomised controlled trials. For five of the biomarkers the effect was less than half as strong in the randomised controlled trial datasets. Across all 31 meta-analyses, on average datasets from observational studies suggested larger prognostic effects than those from randomised controlled trials; from a random effects meta-analysis, the estimated average difference in the effect size was 24% (95% CI 7% to 40%) of the overall biomarker effect.
Cardiovascular biomarkers often have less promising results in the evidence derived from randomised controlled trials than from observational studies.

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    • "Empirical evidence indicates that flaws in the design, conduct, and analysis of trials can lead to bias and distort their effects. Previous meta-epidemiologic studies have assessed the influence of various study characteristics on their effects, including among others indexing in MEDLINE [1], language [2] [3], design [4] [5], methodological characteristics [6], sample size [7e10], and others with most focus on randomized trials. "
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    ABSTRACT: Objectives To examine the influence of the following study characteristics on their study effect estimates: (1) indexing in MEDLINE, (2) language, and (3) design. For randomized trials, (4) trial size and (5) unequal randomization were also assessed. Study Design and Setting The CAtegorical Dental and Maxillofacial Outcome Syntheses meta-epidemiologic study was conducted. Eight databases/registers were searched up to September 2012 for meta-analyses of binary outcomes with at least five studies in the field of dental and maxillofacial medicine. The previously mentioned five study characteristics were investigated. The ratio of odds ratios (ROR) according to each characteristic was calculated with random-effects meta-regression and then pooled across meta-analyses. Results A total of 281 meta-analyses were identified and used to assess the influence of the following factors: non-MEDLINE indexing vs. MEDLINE indexing (n = 78; ROR, 1.12; 95% confidence interval [CI]: 1.05, 1.19; P = 0.001), language (n = 61; P = 0.546), design (n = 24; P = 0.576), small trials (<200 patients) vs. large trials (≥200 patients) (n = 80; ROR, 0.92; 95% CI: 0.87, 0.98; P = 0.009) and unequal randomization (n = 36; P = 0.828). Conclusion Studies indexed in MEDLINE might present greater effects than non-indexed ones. Small randomized trials might present greater effects than large ones.
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    • "Meta-analyses comparing RCTs and observational studies have been conducted with varying aims including comparing treatment effects [111], adverse effects of treatments [112,113] and prognostic factors [114]. However, although the clinical course of low back pain has been studied in observational studies [10,11], we are not aware of a direct comparison with the clinical course of symptoms in RCTs. "
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    ABSTRACT: Evidence suggests that the course of low back pain (LBP) symptoms in randomised clinical trials (RCTs) follows a pattern of large improvement regardless of the type of treatment. A similar pattern was independently observed in observational studies. However, there is an assumption that the clinical course of symptoms is particularly influenced in RCTs by mere participation in the trials. To test this assumption, the aim of our study was to compare the course of LBP in RCTs and observational studies. Source of studies CENTRAL database for RCTs and MEDLINE, CINAHL, EMBASE and hand search of systematic reviews for cohort studies. Studies include individuals aged 18 or over, and concern non-specific LBP. Trials had to concern primary care treatments. Data were extracted on pain intensity. Meta-regression analysis was used to compare the pooled within-group change in pain in RCTs with that in cohort studies calculated as the standardised mean change (SMC). 70 RCTs and 19 cohort studies were included, out of 1134 and 653 identified respectively. LBP symptoms followed a similar course in RCTs and cohort studies: a rapid improvement in the first 6 weeks followed by a smaller further improvement until 52 weeks. There was no statistically significant difference in pooled SMC between RCTs and cohort studies at any time point:- 6 weeks: RCTs: SMC 1.0 (95% CI 0.9 to 1.0) and cohorts 1.2 (0.7to 1.7); 13 weeks: RCTs 1.2 (1.1 to 1.3) and cohorts 1.0 (0.8 to 1.3); 27 weeks: RCTs 1.1 (1.0 to 1.2) and cohorts 1.2 (0.8 to 1.7); 52 weeks: RCTs 0.9 (0.8 to 1.0) and cohorts 1.1 (0.8 to 1.6). The clinical course of LBP symptoms followed a pattern that was similar in RCTs and cohort observational studies. In addition to a shared 'natural history', enrolment of LBP patients in clinical studies is likely to provoke responses that reflect the nonspecific effects of seeking and receiving care, independent of the study design.
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    • "Evidence from Framingham studies showed that some disorders and conditions (such as hypertension, hyperlipidaemia, smoking, diabetes, old age, and male sex) are particularly useful to estimate the cardiovascular (CV) risk of acute ischemic events [1] and are currently considered as the " traditional " cardiovascular risk factors. This led to the development of several clinically based CV risk stratification tools, and the Framingham risk score is one of the most commonly used CV risk stratification tools nowadays [2]. However, these " traditional " cardiovascular risk factors were shown to be suboptimal for proper CV risk stratification due to low specificity and sensitivity [3] [4] [5] [6]. "
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    ABSTRACT: This research was funded by EU FP7, Grant no. 201668, AtheroRemo to Dr. F. Mach. This work was also supported by the Swiss National Science Foundation Grants to Dr. F. Mach (no. 310030-118245) and Dr. F. Montecucco (no. 32003B-134963/1).
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