Shortest path or random walks? A framework for path weights in network meta-analysis.

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta-analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path-design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite set of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For large networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path-weights framework also has the potential to answer more general research questions in NMA.

To adjust or not to adjust in living systematic reviews? It's all about the context.

With each update of meta-analyses from living systematic reviews, treatment effects and their confidence intervals are recalculated. This often raises the question whether or not multiplicity is an issue and whether a method to adjust for multiplicity is needed. It seems that answering these questions is not that straightforward. We approach this matter by considering the context of systematic reviews and pointing out existing methods for handling multiplicity in meta-analysis. We conclude that multiplicity is not a relevant issue in living systematic reviews when they are planned with the aim to provide up-to-date evidence, without any direct control on the decision over future research. Multiplicity might be an issue, though, in living systematic reviews designed under a protocol involving a "stopping decision", which can be the case in living guideline development or in reimbursement decisions. Several appropriate methods exist for handling multiplicity in meta-analysis. Existing methods, however, are also associated with several technical and conceptual limitations, and could be improved in future methodological projects. To better decide whether an adjustment for multiplicity is necessary at all, authors and users of living systematic reviews should be aware of the context of the work and question whether there is a dependency between the effect estimates of the living systematic review and its stopping/updating or an influence on future research.

Evaluating agreement between evidence from randomised controlled trials and cohort studies in nutrition: a meta-research replication study.

This meta-research study aims to evaluate the agreement of effect estimates between bodies of evidence (BoE) from RCTs and cohort studies included in the same nutrition evidence synthesis, to identify factors associated with disagreement, and to replicate the findings of a previous study. We searched Medline, Epistemonikos and the Cochrane Database of Systematic Reviews for nutrition systematic reviews that included both RCTs and cohort studies for the same patient-relevant outcome or intermediate-disease marker. We rated similarity of PI/ECO (population, intervention/exposure, comparison, outcome) between BoE from RCTs and cohort studies. Agreement of effect estimates across BoE was analysed by pooling ratio of risk ratios (RRR) for binary outcomes and difference of standardised mean differences (DSMD) for continuous outcomes. We performed subgroup and sensitivity analyses to explore determinants associated with disagreements. We included 82 BoE-pairs from 51 systematic reviews. For binary outcomes, the RRR was 1.04 (95% confidence interval (CI) 0.99 to 1.10, I2 = 59%, τ2 = 0.02, prediction interval (PI) 0.77 to 1.41). For continuous outcomes, the pooled DSMD was - 0.09 (95% CI - 0.26 to 0.09, PI - 0.55 to 0.38). Subgroup analyses yielded that differences in type of intake/exposure were drivers towards disagreement. We replicated the findings of a previous study, where on average RCTs and cohort studies had similar effect estimates. Disagreement and wide prediction intervals were mainly driven by PI/ECO-dissimilarities. More research is needed to explore other potentially influencing factors (e.g. risk of bias) on the disagreement between effect estimates of both BoE.Trial registration: CRD42021278908.