arXiv subjects

Adriani Nikolakopoulou

Publications and source records attributed to Adriani Nikolakopoulou.

8 recordsLinked to original sources

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.

stat.ME↗

arXiv2026

Creating treatment and component hierarchies in component network meta-analysis

Component network meta-analysis (CNMA) is a statistical methodology that enables estimation of relative effects for multi-component treatments, such as combinations of antidepressants, and individual components, such as single antidepressants, by synthesizing data from multiple studies. A commonly desired output of a systematic review and meta-analysis is a hierarchy of the treatments in terms of a certain performance metric. Methods have been established for standard network meta-analysis (NMA), but have not yet been extended to CNMA. In particular, CNMA presents unique challenges because the set of relative effects that can be uniquely estimated is more complex to determine compared to standard NMA, and a hierarchy involving relative effects that are not uniquely estimable is misleading. We present a step-by-step workflow for answering treatment hierarchy questions in both frequentist and Bayesian CNMA, including explicitly identifying the uniquely estimable relative effects. We illustrate the workflow by posing multiple treatment hierarchy questions in two distinct networks, one concerning primary care of depression and one disconnected network investigating treatment for chronic lymphocytic leukemia.

stat.ME↗

arXiv2025

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↗

arXiv2025

Path-Based Approach for Detecting and Assessing Inconsistency in Network Meta-Analysis: A Novel Method

Network Meta-Analysis (NMA) plays a pivotal role in synthesizing evidence from various sources and comparing multiple interventions. At its core, NMA relies on integrating both direct evidence from head-to-head comparisons and indirect evidence from different paths that link treatments through common comparators. A key aspect is evaluating consistency between direct and indirect sources. Existing methods to detect inconsistency, although widely used, have limitations. For example, they do not account for differences within indirect sources or cannot estimate inconsistency when direct evidence is absent. In this paper, we introduce a path-based approach that explores all sources of evidence without separating direct and indirect. We introduce a measure based on the square of differences to quantitatively capture inconsistency, and propose a Netpath plot to visualize inconsistencies between various paths. We provide an implementation of our path-based method within the netmeta R package. Via application to fictional and real-world examples, we show that our method is able to detect and visualize inconsistency between multiple paths of evidence that would otherwise be masked by considering all indirect sources together. The path-based approach therefore provides a more comprehensive evaluation of inconsistency within a network of treatments.

stat.ME↗

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↗

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↗

arXiv2021

Network meta-analysis and random walks

Network meta-analysis (NMA) is a central tool for evidence synthesis in clinical research. The results of an NMA depend critically on the quality of evidence being pooled. In assessing the validity of an NMA, it is therefore important to know the proportion contributions of each direct treatment comparison to each network treatment effect. The construction of proportion contributions is based on the observation that each row of the hat matrix represents a so-called 'evidence flow network' for each treatment comparison. However, the existing algorithm used to calculate these values is associated with ambiguity according to the selection of paths. In this work we present a novel analogy between NMA and random walks. We use this analogy to derive closed-form expressions for the proportion contributions. A random walk on a graph is a stochastic process that describes a succession of random 'hops' between vertices which are connected by an edge. The weight of an edge relates to the probability that the walker moves along that edge. We use the graph representation of NMA to construct the transition matrix for a random walk on the network of evidence. We show that the net number of times a walker crosses each edge of the network is related to the evidence flow network. By then defining a random walk on the directed evidence flow network, we derive analytically the matrix of proportion contributions. The random-walk approach, in addition to being computationally more efficient, has none of the associated ambiguity of the existing algorithm.

cs.SI↗

arXiv2020

Introducing the treatment hierarchy question in network meta-analysis

Background: Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the least to most preferable option. However, the research question associated with the hierarchy of multiple interventions is never clearly defined in published reviews. Methods and Results: We introduce the notion of a treatment hierarchy question that describes the criterion for choosing a specific treatment over one or more competing alternatives. For example, stakeholders might ask which treatment is most likely to improve mean survival by at least 2 years or which treatment is associated with the longest mean survival. The answers to these two questions are not necessarily the same. We discuss the most commonly used ranking metrics (quantities that describe or compare the estimated treatment-specific effects), how the metrics produce a treatment hierarchy and the type of treatment hierarchy question that each metric can answer. We show that the ranking metrics encompass the uncertainty in the estimation of the treatment effects in different ways, which results in different treatment hierarchies. Conclusions: Network meta-analyses that aim to rank treatments should state in the protocol the treatment hierarchy question they aim to address and employ the appropriate ranking metric to answer it.

stat.ME↗