Dataset Information

Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

ABSTRACT: Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

SUBMITTER: Bartos F

PROVIDER: S-EPMC10087723 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

Bartoš František F Maier Maximilian M Wagenmakers Eric-Jan EJ Doucouliagos Hristos H Stanley T D TD

Research synthesis methods 20220807 1

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, ...[more]

PMID: 35869696

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Project description:Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

Project description:Publication bias is prevalent within the scientific literature. Whilst there are multiple ideas on how to reduce publication bias, only a minority of journals have made substantive changes to address the problem. We aimed to explore the perceived feasibility of strategies to reduce publication bias by gauging opinions of journal editors (n = 73) and other academics/researchers (n = 160) regarding nine methods of publishing and peer-reviewing research: mandatory publication, negative results journals/articles, open reviewing, peer-review training and accreditation, post-publication review, pre-study publication of methodology, published rejection lists, research registration, and two-stage review. Participants completed a questionnaire asking both quantitative (multiple choice or Likert scales) and qualitative (open-ended) questions regarding the barriers to implementing each suggestion, and their strengths and limitations. Participants were asked to rate the nine suggestions, then choose the method they felt was most effective. Mandatory publication was most popularly selected as the 'most effective' method of reducing publication bias for editors (25%), and was the third most popular choice for academics/researchers (14%). The most common selection for academics/researchers was two-stage review (26%), but fewer editors prioritised this (11%). Negative results journals/articles were the second and third most common choices for academics/researchers (21%) and editors (16%), respectively. Editors more commonly chose research registration as 'most effective' (21%), which was favoured by only 6% of academics/researchers. Whilst mandatory publication was generally favoured by respondents, it is infeasible to trial at a journal level. Where suggestions have already been implemented (e.g. negative results journals/articles, trial registration), efforts should be made to objectively assess their efficacy. Two-stage review should be further trialled as its popularity amongst academics/researchers suggests it may be well received, though editors may be less receptive. Several underlying barriers to change also emerged, including scientific culture, impact factors, and researcher training; these should be further explored to reduce publication bias.

Dataset Information

Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

Publications

Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

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