Before embarking on an individual participant data meta‐analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here, we suggest how to estimate the power of a planned IPDMA of randomised trials aiming to examine treatment‐covariate interactions at the participant‐level (i.e., treatment effect modifiers). We focus on a time‐to‐event (survival) outcome with a binary or continuous covariate, and propose an approximate analytic power calculation that conditions on the actual characteristics of trials, for example, in terms of sample sizes and covariate distributions. The proposed method has five steps: (i) extracting the following aggregate data for each group in each trial—the number of participants and events, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate; (ii) specifying a minimally important interaction size; (iii) deriving an approximate estimate of Fisher's information matrix for each trial and the corresponding variance of the interaction estimate per trial, based on assuming an exponential survival distribution; (iv) deriving the estimated variance of the summary interaction estimate from the planned IPDMA, under a common‐effect assumption, and (v) calculating the power of the IPDMA based on a two‐sided Wald test. Stata and R code are provided and a real example provided for illustration. Further evaluation in real examples and simulations is needed.

Citation tracking (CT) collects references with citation relationships to pertinent references that are already known. This scoping review maps the benefit of and the tools and terminology used for CT in health-related systematic literature searching. We included methodological studies on evidence retrieval by CT in health-related literature searching without restrictions on study design, language, or publication date. We searched MEDLINE/Ovid, Web of Science Core Collection, CINAHL/EBSCOhost, LLISFT/EBSCOhost, LISTA/EBSCOhost, conducted web searching via Google Scholar, backward/forward CT of included studies and pertinent reviews, and contacting of experts. Two reviewers independently assessed eligibility. Data extraction and analysis were performed by one reviewer and checked by another. We screened 11,861 references and included 47 studies published between 1985 and 2021. Most studies (96%) assessed the benefit of CT either as supplementary or primary/stand-alone search method. Added value of CT for evidence retrieval was found by 96% of them. Science Citation Index and Social Sciences Citation Index were the most common citation indexes used. Application of multiple citation indexes in parallel, co-citing or co-cited references, CT iterations, or software tools was rare. CT terminology was heterogeneous and frequently ambiguous. The use of CT showed an added value in most of the identified studies; however, the benefit of CT in health-related systematic literature searching likely depends on multiple factors that could not be assessed with certainty. Application, terminology, and reporting are heterogeneous. Based on our results, we plan a Delphi study to develop recommendations for the use and reporting of CT.

When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To overcome these challenges, we introduce Bayesian Regularized Meta-Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many potential moderators, but it is not known beforehand which of them are relevant. A simulation study compared BRMA against state-of-the-art random effects meta-regression using restricted maximum likelihood (RMA). Results indicated that BRMA outperformed RMA on three metrics: BRMA had superior predictive performance, which means that the results generalized better; BRMA was better at rejecting irrelevant moderators, and worse at detecting true effects of relevant moderators, while the overall proportion of Type I and Type II errors was equivalent to RMA. BRMA regression coefficients were slightly biased towards zero (by design), but its residual heterogeneity estimates were less biased than those of RMA. BRMA performed well with as few as 20 studies, suggesting its suitability as a small sample solution. We present free open source software implementations in the R-package pema (for penalized meta-analysis) and in the stand-alone statistical program JASP. An applied example demonstrates the use of the R-package.

Matching-adjusted indirect comparison (MAIC) enables indirect comparisons of interventions across separate studies when individual patient-level data (IPD) are available for only one study. Due to its similarity with propensity score weighting, it has been speculated that MAIC can be combined with outcome regression models in the spirit of augmented inverse probability weighting estimators to improve robustness and efficiency. We show that MAIC enjoys intrinsic double-robustness and semiparametric efficiency properties for estimating the average treatment effect on the treated in the limited IPD setting without explicit augmentation. A connection between MAIC and the method of simulated treatment comparisons is highlighted. These results clarify conditions under which MAIC is consistent and efficient, informing appropriate application and interpretation of MAIC analyses.

The number of clinical prediction models sharing the same prediction task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these prediction models have not been sufficiently studied, particularly in the context of meta-analysis settings where only summary statistics are available. In particular, we consider the following situation: we want to predict an outcome Y, that is not included in our current data, while the covariate data are fully available. In addition, the summary statistics from prior studies, which share the same prediction task (i.e., the prediction of Y), are available. This study introduces a new method for synthesizing the summary results of binary prediction models reported in the prior studies using a linear predictor under a distributional assumption between the current and prior studies. The method provides an integrated predictor combining all predictors reported in the prior studies with weights. The vector of the weights is designed to achieve the hypothetical improvement of area under the receiver operating characteristic curve (AUC) on the current available data under a practical situation where there are different sets of covariates in the prior studies. We observe a counterintuitive aspect in typical situations where a part of weight components in the proposed method becomes negative. It implies that flipping the sign of the prediction results reported in each individual study would improve the overall prediction performance. Finally, numerical and real-world data analysis were conducted and showed that our method outperformed conventional methods in terms of AUC.

We investigated to which degree commercial funding is associated with estimated intervention effects in randomized trials. We included meta-epidemiological studies with published data on the association between commercial funding and results or conclusions of randomized trials. We searched five databases and other sources. We selected one result per meta-epidemiological study, preferably unadjusted ratio of odds ratios (ROR), for example, odds ratio(commercial funding)/odds ratio(noncommercial funding). We pooled RORs in random-effects meta-analyses (ROR <1 indicated exaggerated intervention effects in commercially funded trials), subgrouped (preplanned) by study aim: commercial funding per se versus risk of commercial funder influence. We included eight meta-epidemiological studies (264 meta-analyses, 2725 trials). The summary ROR was 0.95 (95% confidence interval 0.85-1.06). Subgroup analysis revealed a difference (p=0.02) between studies of commercial funding per se, ROR 1.06 (0.95-1.17) and studies of risk of commercial funder influence, ROR 0.88 (0.79-0.97). In conclusion, we found no statistically significant association between commercial funding and estimated intervention effects when combining studies of commercial funding per se and studies of risk of commercial funder influence. A preplanned subgroup analysis indicated that trials with high risk of commercial funder influence exaggerated intervention effects by 12% (21%-3%), on average. Our results differ from previous theoretical considerations and findings from methodological studies and therefore call for confirmation. We suggest it is prudent to interpret results from commercially funded trials with caution, especially when there is a risk that the funder had direct influence on trial design, conduct, analysis, or reporting.

Enhanced uptake of systematic reviews that use qualitative comparative analyses (QCA) requires knowing how end-users interpret such findings. The study purpose was to identify effective approaches to communicating results from a QCA within a systematic review. Sequential exploratory mixed methods design; thematic analysis of interviews with 11 end-users followed by a randomized experiment with 254 participants that provided QCA results for a hypothetical review presented through three formats (text, table, and figure). A survey administered after the experiment assessed subjective and objective comprehension of QCA results. Interview themes included use of jargon; appropriate use of appendices, tables, figures; and integration of QCA results within the systematic review. In the experiment, we observed a significant difference (p=0.035) in subjective comprehension across the three presentation formats. Participants randomized to the figure and text formats scored higher compared to the table. No significant differences were observed for objective comprehension overall (p=0.11). However, for parameter interpretation (a unique component of QCA results), scores among participants that received the figure format were significantly higher than scores for participants who received the text (p=0.001) or table (p=0.004). No significant differences (p=0.09) were observed in objective comprehension for configuration interpretation. End-users of systematic reviews saw value in the use of QCA, but unfamiliar methods and terminology were barriers to full understanding of the findings. When presenting results, a figure format appears to be superior to text or table formats based on measures of subjective comprehension and some measures of objective comprehension.

Research Synthesis MethodsEarly View LETTER TO THE EDITOR Pitfalls in choosing data examples for methodological work: Bayesian approaches to a fixed effects meta-analysis of zinc lozenges for the common cold Harri Hemilä, Corresponding Author Harri Hemilä harri.hemila@helsinki.fi orcid.org/0000-0002-4710-307X Department of Public Health, University of Helsinki, Helsinki, Finland Correspondence Harri Hemilä, Department of Public Health, University of Helsinki, POB 20, Helsinki FI-00014, Finland. Email: harri.hemila@helsinki.fiSearch for more papers by this authorElizabeth Chalker, Elizabeth Chalker orcid.org/0000-0003-1593-3770 Biological Data Science Institute, Australian National University, Canberra, AustraliaSearch for more papers by this author Harri Hemilä, Corresponding Author Harri Hemilä harri.hemila@helsinki.fi orcid.org/0000-0002-4710-307X Department of Public Health, University of Helsinki, Helsinki, Finland Correspondence Harri Hemilä, Department of Public Health, University of Helsinki, POB 20, Helsinki FI-00014, Finland. Email: harri.hemila@helsinki.fiSearch for more papers by this authorElizabeth Chalker, Elizabeth Chalker orcid.org/0000-0003-1593-3770 Biological Data Science Institute, Australian National University, Canberra, AustraliaSearch for more papers by this author First published: 12 September 2022 https://doi.org/10.1002/jrsm.1601Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately-which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups.