arXiv subjects

Guido Schwarzer

Publications and source records attributed to Guido Schwarzer.

7 recordsLinked to original sources

arXiv2026

Frequentist prediction intervals for random-effects meta-analysis via confidence-distribution propagation

Prediction intervals are increasingly recommended in random-effects meta-analysis because they describe the range of true effects expected in a future study or setting. Conventional frequentist intervals can have inadequate finite-sample coverage because uncertainty in the between-study variance is not fully propagated. We propose a confidence-distribution propagation method that carries uncertainty through the random-effects hierarchy. The method samples the between-study variance from a confidence distribution obtained by inverting the exact distribution of Cochran's Q and, conditional on each draw, samples the average effect from its corresponding normal confidence distribution before generating a future true effect. Prediction limits are empirical quantiles of the resulting Monte Carlo distribution. Across the scenarios examined, the proposed method improved or maintained coverage relative to existing frequentist intervals, including the Nagashima-Noma-Furukawa confidence-distribution bootstrap, with generally modest increases in expected width. The same Monte Carlo sample also yields confidence intervals for the average effect and heterogeneity measures. The method is implemented in the R package cdmeta.

stat.ME↗

arXiv2026

Assessing the Impact of Model Assumptions in Network Meta-Regression: A Simulation Study

Network meta-regression (NMR) extends network meta-analysis (NMA) by synthesizing evidence on multiple treatments while adjusting for potential effect modifiers. By accounting for effect modification, NMR can reduce between-study heterogeneity and improve the validity of relative treatment effects, providing insight regarding characteristics impacting treatment performance. However, choosing between available NMR models is complex, as each model addresses a similar, but unique research question, and the performance of available NMR models under varying network structures, between-study heterogeneity, and interaction assumptions remains unclear. We evaluated the consequences of model misspecification in a simulation study of 120 evidence-network scenarios designed to reflect potential complications in evidence networks introduced by trial design, heterogeneity levels, and interaction assumptions. We compared the standard interaction-free NMA model with four NMR parameterizations differing in across-comparison interaction assumptions (common vs. independent interactions) and interaction consistency assumptions (with or without consistency). Standard NMA models generally overestimated treatment effects when effect modification was present. NMR models with independent across-comparison interactions maintained appropriate confidence interval coverage in dense networks generated with their corresponding consistency assumptions. However, their coverage deteriorated in sparse networks with between-study heterogeneity. Models assuming consistent interactions are advantageous in networks with multi-arm studies. Ignoring effect modification in NMA can lead to biased treatment effect estimates. When effect modification is anticipated, thoughtful alignment between network structure and NMR assumptions can reduce bias and misleading precision, supporting more reliable medical decision making.

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arXiv2026

Network Meta-analysis and Diffusion

We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a number of visualization tools implemented in R.

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arXiv2025

Precision of Treatment Hierarchy: A Metric for Quantifying Certainty in Treatment Hierarchies from Network Meta-Analysis

Network meta-analysis (NMA) is an extension of pairwise meta-analysis which facilitates the estimation of relative effects for multiple competing treatments. A hierarchy of treatments is a useful output of an NMA. Treatment hierarchies are produced using ranking metrics. Common ranking metrics include the Surface Under the Cumulative RAnking curve (SUCRA) and P-scores, which are the frequentist analogue to SUCRAs. Both metrics consider the size and uncertainty of the estimated treatment effects, with larger values indicating a more preferred treatment. Although SUCRAs and P-scores themselves consider uncertainty, treatment hierarchies produced by these ranking metrics are typically reported without a measure of certainty, which might be misleading to practitioners. We propose a new metric, Precision of Treatment Hierarchy (POTH), which quantifies the certainty in producing a treatment hierarchy from SUCRAs or P-scores. The metric connects three statistical quantities: The variance of the SUCRA values, the variance of the mean rank of each treatment, and the average variance of the distribution of individual ranks for each treatment. POTH provides a single, interpretable value which quantifies the degree of certainty in producing a treatment hierarchy. We show how the metric can be adapted to apply to subsets of treatments in a network, for example, to quantify the certainty in the hierarchy of the top three treatments. We calculate POTH for a database of NMAs to investigate its empirical properties, and we demonstrate its use on three published networks.

stat.ME↗

arXiv2024

Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria

A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for uncertainty, over-emphasizing small differences in treatment effects. We propose a novel framework to estimate treatment hierarchies in NMA using a probabilistic model, focusing on a clinically relevant treatment-choice criterion (TCC). Initially, we formulate a mathematical expression to define a TCC based on smallest worthwhile differences (SWD), converting NMA relative treatment effects into treatment preference format. This data is then synthesized using a probabilistic ranking model, assigning each treatment a latent 'ability' parameter, representing its propensity to yield clinically important and beneficial true treatment effects relative to the rest of the treatments in the network. Parameter estimation relies on the maximum likelihood theory, with standard errors derived asymptotically from Fisher's information matrix. To facilitate the use of our methods, we launched the R package mtrank. We applied our method to two clinical datasets: one comparing 18 antidepressants for major depression and another comparing 6 antihypertensives for the incidence of diabetes. Our approach provided robust, interpretable treatment hierarchies that account for a concrete TCC. We further examined the agreement between the proposed method and existing ranking metrics in 153 published networks, concluding that the degree of agreement depends on the precision of the NMA estimates. Our framework offers a valuable alternative for NMA treatment ranking, mitigating over-interpretation of minor differences. This enables more reliable and clinically meaningful treatment hierarchies.

stat.ME↗

arXiv2023

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 to the contributions of paths to NMA estimates. 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 space of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For complex 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 network meta-analysis.

physics.soc-ph↗

arXiv2022

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Network meta-analysis (NMA) is widely used in evidence synthesis to estimate the effects of several competing interventions for a given clinical condition. One of the challenges is that it is not possible in disconnected networks. Component network meta-analysis (CNMA) allows technically 'reconnecting' a disconnected network with multicomponent interventions. The additive CNMA model assumes that the effect of any multicomponent intervention is the additive sum of its components. This assumption can be relaxed by adding interaction component terms, which improves the goodness of fit but decreases the network connectivity. Model selection aims at finding the model with a reasonable balance between the goodness of fit and connectivity (selected CNMA model). We aim to introduce a forward model selection strategy for CNMA models and to investigate the performance of CNMA models for connected and disconnected networks. We applied the methods to a real Cochrane review dataset and simulated data with additive, mildly, or strongly violated intervention effects. We started with connected networks, and we artificially constructed disconnected networks. We compared the results of the additive and the selected CNMAs from each connected and disconnected network with the NMA using the mean squared error and coverage probability. CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. On the contrary, model selection does not perform well for disconnected networks, and we recommend conducting separate analyses of subnetworks.

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