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Impact of heterogeneity and effect size on the estimation of the optimal information size: analysis of recently published meta-analyses.

ABSTRACT: OBJECTIVE:To estimate the proportion of systematic reviews that meet the optimal information size (OIS) and assess the impact heterogeneity and effect size have on the OIS estimate by type of outcome (eg, mortality, semiobjective or subjective). METHODS:We carried out searches of Medline and Cochrane to retrieve meta-analyses published in systematic reviews from 2010 to 2012. We estimated the OIS using Trial Sequential Analysis software (TSA V.0.9) and based on several heterogeneity and effect size scenarios, stratifying by type of outcome (mortality/semiobjective/subjective) and by Cochrane/non-Cochrane reviews. RESULTS:We included 137 meta-analyses out of 218 (63%) potential systematic reviews (one meta-analysis from each systematic review). Of these reviews, 83 (61%) were Cochrane and 54 (39%) non-Cochrane. The Cochrane reviews included a mean of 6.5 (SD 6.1) studies and the non-Cochrane included a mean of 13.2 (SD 10.2) studies. The mean number of patients was 2619.1 (SD 6245.8 or median 586.0) for the Cochrane and 19?888.5 (SD 32 925.7 or median 6566.5) patients for the non-Cochrane reviews. The percentage of systematic reviews that achieved the OIS for all-cause mortality outcome were 0% Cochrane and 25% for non-Cochrane reviews; for semiobjective outcome 17% for Cochrane and 46% for non-Cochrane reviews and for subjective outcome 45% for Cochrane and 72% for non-Cochrane reviews. CONCLUSIONS:The number of systematic reviews that meet an optimal information size is low and varies depending on the type of outcome and the type of publication. Less than half of primary outcomes synthesised in systematic reviews achieve the OIS, and therefore the conclusions are subject to substantial uncertainty.

SUBMITTER: Garcia-Alamino JM

PROVIDER: S-EPMC5695413 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Impact of heterogeneity and effect size on the estimation of the optimal information size: analysis of recently published meta-analyses.

Garcia-Alamino Josep M JM Bankhead Clare C Heneghan Carl C Pidduck Nicola N Perera Rafael R

BMJ open 20171108 11

<h4>Objective</h4>To estimate the proportion of systematic reviews that meet the optimal information size (OIS) and assess the impact heterogeneity and effect size have on the OIS estimate by type of outcome (eg, mortality, semiobjective or subjective).<h4>Methods</h4>We carried out searches of Medline and Cochrane to retrieve meta-analyses published in systematic reviews from 2010 to 2012. We estimated the OIS using <i>Trial Sequential Analysis</i> software (TSA V.0.9) and based on several hete ...[more]

PMID: 29122784

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Project description:BackgroundThe analysis of large-scale gene expression data is a fundamental approach to functional genomics and the identification of potential drug targets. Results derived from such studies cannot be trusted unless they are adequately designed and reported. The purpose of this study is to assess current practices on the reporting of experimental design and statistical analyses in gene expression-based studies.MethodsWe reviewed hundreds of MEDLINE-indexed papers involving gene expression data analysis, which were published between 2003 and 2005. These papers were examined on the basis of their reporting of several factors, such as sample size, statistical power and software availability.ResultsAmong the examined papers, we concentrated on 293 papers consisting of applications and new methodologies. These papers did not report approaches to sample size and statistical power estimation. Explicit statements on data transformation and descriptions of the normalisation techniques applied prior to data analyses (e.g. classification) were not reported in 57 (37.5%) and 104 (68.4%) of the methodology papers respectively. With regard to papers presenting biomedical-relevant applications, 41(29.1 %) of these papers did not report on data normalisation and 83 (58.9%) did not describe the normalisation technique applied. Clustering-based analysis, the t-test and ANOVA represent the most widely applied techniques in microarray data analysis. But remarkably, only 5 (3.5%) of the application papers included statements or references to assumption about variance homogeneity for the application of the t-test and ANOVA. There is still a need to promote the reporting of software packages applied or their availability.ConclusionRecently-published gene expression data analysis studies may lack key information required for properly assessing their design quality and potential impact. There is a need for more rigorous reporting of important experimental factors such as statistical power and sample size, as well as the correct description and justification of statistical methods applied. This paper highlights the importance of defining a minimum set of information required for reporting on statistical design and analysis of expression data. By improving practices of statistical analysis reporting, the scientific community can facilitate quality assurance and peer-review processes, as well as the reproducibility of results.

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Impact of heterogeneity and effect size on the estimation of the optimal information size: analysis of recently published meta-analyses.

Publications

Impact of heterogeneity and effect size on the estimation of the optimal information size: analysis of recently published meta-analyses.

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