Dataset Information

Bayesian mixed treatment comparisons meta-analysis for correlated outcomes subject to reporting bias.

ABSTRACT: Many randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of RCTs have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared to univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e., when the number of treatments is two). Current methods for mixed treatment comparisons (MTC) meta-analysis (i.e., when the number of treatments is more than two) have focused on univariate or very recently, bivariate outcomes. To broaden their application, we propose a framework for MTC meta-analysis of multivariate (? 2) outcomes where the correlations among multivariate outcomes within- and between-studies are accounted for through copulas, and the joint modeling of multivariate random effects, respectively. We consider a Bayesian hierarchical model using Markov Chain Monte Carlo methods for estimation. An important feature of the proposed framework is that it allows for borrowing of information across correlated outcomes. We show via simulation that our approach reduces the impact of outcome reporting bias (ORB) in a variety of missing outcome scenarios. We apply the method to a systematic review of RCTs of pharmacological treatments for alcohol dependence, which tends to report multiple outcomes potentially subject to ORB.

SUBMITTER: Liu Y

PROVIDER: S-EPMC5846497 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

Bayesian mixed treatment comparisons meta-analysis for correlated outcomes subject to reporting bias.

Liu Yulun Y DeSantis Stacia M SM Chen Yong Y

Journal of the Royal Statistical Society. Series C, Applied statistics 20170317 1

Many randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of RCTs have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared to univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e., when the number of treat ...[more]

PMID: 29540936

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Project description:ObjectivesTo identify published closed-loop Bayesian mixed treatment comparisons (MTCs) and to summarise characteristics regarding their conduct and reporting.DesignSystematic review.MethodsWe searched multiple bibliographic databases (January 2006-31 July 2011) for full-text, English language publications of Bayesian MTCs comparing the effectiveness or safety of ?3 interventions based on randomised controlled trials and having at least one closed loop. Methodological and reporting characteristics of MTCs were extracted in duplicate and summarised descriptively.ResultsWe identified 34 Bayesian MTCs spanning 13 clinical areas. Publication of MTCs increased over the 5-year period; with 76.5% published during or after 2009. MTCs included a mean (±SD) of 35.9±30.1 trials (n=33 459±71 233 participants) and 8.5±4.3 interventions (85.7% pharmacological). Non-informative and informative prior distributions were reported to be used in 44.1% and 8.8% of MTCs, respectively, with the remainder failing to specify the prior used. A random-effects model was used to analyse the networks of trials in 58.5% of MTCs, all using WinBUGS; however, code was infrequently provided (20.6%). More than two-thirds of MTCs (76.5%) also conducted traditional meta-analysis. Methods used to evaluate convergence, heterogeneity and inconsistency were infrequently reported, but from those providing detail, methods appeared varied. MTCs most often used a binary effect measure (85.3%) and ranking of interventions based on probability was common (61.8%), although rarely displayed in a figure (8.8% of MTCs). MTCs were published in 24 different journals with a mean impact factor of 9.20±8.71. While 70.8% of journals imposed limits on word counts and 45.8% limits on the number of tables/figures, online supplements/appendices were allowed in 79.2% of journals. Publication of closed-loop Bayesian MTCs is increasing in frequency, but details regarding their methodology are often poorly described. Efforts in clarifying the appropriate methods and reporting of Bayesian MTCs should be of priority.

Project description:Background & aimsIn 2005, the registration of all randomised controlled trials (RCTs) before enrolment of participants became a condition for publication by the International Committee of Medical Journal Editors to increase transparency in trial reporting. Among RCTs on transarterial chemoembolisation (TACE) for the treatment of hepatocellular carcinoma (HCC) published after 2007, we assess the proportion that were registered and compare registered primary outcomes (PO) with those reported in publications to determine whether primary outcome reporting bias favoured significant outcomes.MethodsWe searched MEDLINE and EMBASE for reports of RCTs evaluating TACE for HCC treatment between 1 September 2007 and 31 March 2018. Registration and publication information for each included RCT was compared using a standardised data extraction form.ResultsThirteen out of 53 (25%) included RCTs were correctly registered (i.e. before the starting date of the RCT), 14 (26%) were registered after the RCT starting date, and 26 (49%) were not registered. Six out of 14 of the retrospectively registered RCTs (43%) were registered after their completion date. The PO was clearly reported in the published article of all registered RCTs, whereas the report was not clear in 8/26 (31%) of the non-registered RCTs (p = 0.01). Among registered RCTs, 8/27 (30%) had major discrepancies between registered and published PO. The influence of these discrepancies could be assessed in 6 of them and was shown to statistically favour significant results in 2.ConclusionsRegistration and outcome reporting in RCTs on TACE for HCC are often inadequate. Registration should be reinforced because it is a key to transparency.Lay summaryTrial registration is fundamental to our understanding and interpretation of results, as it provides information on all relevant clinical trials (to place the results in a broader context), and on the details of their associated protocols (to ensure that the scientific plan is followed). Once a randomised controlled trial (RCT) is completed, the trial results are usually publicly shared via scientific articles that are expected to thoroughly and objectively report them. This study shows that half of the RCTs evaluating transarterial chemoembolisation for hepatocellular carcinoma were not registered, and identified major discrepancies between registered and published primary outcome favouring significant results.

Project description:BackgroundNetwork meta-analysis (NMA), a generalization of conventional MA, allows for assessing the relative effectiveness of multiple interventions. Reporting bias is a major threat to the validity of MA and NMA. Numerous methods are available to assess the robustness of MA results to reporting bias. We aimed to extend such methods to NMA.MethodsWe introduced 2 adjustment models for Bayesian NMA. First, we extended a meta-regression model that allows the effect size to depend on its standard error. Second, we used a selection model that estimates the propensity of trial results being published and in which trials with lower propensity are weighted up in the NMA model. Both models rely on the assumption that biases are exchangeable across the network. We applied the models to 2 networks of placebo-controlled trials of 12 antidepressants, with 74 trials in the US Food and Drug Administration (FDA) database but only 51 with published results. NMA and adjustment models were used to estimate the effects of the 12 drugs relative to placebo, the 66 effect sizes for all possible pair-wise comparisons between drugs, probabilities of being the best drug and ranking of drugs. We compared the results from the 2 adjustment models applied to published data and NMAs of published data and NMAs of FDA data, considered as representing the totality of the data.ResultsBoth adjustment models showed reduced estimated effects for the 12 drugs relative to the placebo as compared with NMA of published data. Pair-wise effect sizes between drugs, probabilities of being the best drug and ranking of drugs were modified. Estimated drug effects relative to the placebo from both adjustment models were corrected (i.e., similar to those from NMA of FDA data) for some drugs but not others, which resulted in differences in pair-wise effect sizes between drugs and ranking.ConclusionsIn this case study, adjustment models showed that NMA of published data was not robust to reporting bias and provided estimates closer to that of NMA of FDA data, although not optimal. The validity of such methods depends on the number of trials in the network and the assumption that conventional MAs in the network share a common mean bias mechanism.

Dataset Information

Bayesian mixed treatment comparisons meta-analysis for correlated outcomes subject to reporting bias.

Publications

Bayesian mixed treatment comparisons meta-analysis for correlated outcomes subject to reporting bias.

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