Meta-analysis is a widely used statistical tool for estimating the diagnostic accuracy of tests across multiple studies. Existing methods and available R packages primarily focus on a single diagnostic test, typically under the assumption that all studies include a gold standard. Greater efficiency can be achieved by modeling multiple diagnostic tests together and drawing on studies with or without a gold standard reference test across diverse designs. To address this challenge, recent work has extended both the Bayesian hierarchical model and the Bayesian hierarchical summary receiver operating characteristic model to the framework of network meta-analysis of diagnostic tests, enabling simultaneous comparison of multiple tests when some data are missing. Despite the importance of these methods, their computational complexity has limited their broad application. This article introduces NMADTA, an R package that implements these models with user-friendly functions. The package allows researchers to evaluate the accuracy of multiple diagnostic tests simultaneously and provides comprehensive graphical displays of the results.

Under the network meta-analysis (NMA) framework for aggregate data, there are limited possibilities for evidence synthesis across multiple treatment doses or timepoints. Model-based network meta-analysis (MBNMA) has been recommended as a framework for either evidence synthesis across multiple dose levels or across multiple timepoints to circumvent the limitations of the NMA. A joint dose–response and time–course MBNMA (DT-MBNMA) is proposed that combines the strengths of both DT-MBNMA. This framework allows for combining data at multiple timepoints from studies in early clinical development with a broad range of doses to late-stage clinical studies with a limited range of doses. The method respects randomization and allows for assessment of consistency and hence satisfies the requirements from reimbursement agencies. The method was validated in a simulation study, showing that the drug effect parameters and therefore indirect treatment effects could be recovered without bias, while the precision of the treatment effects was dependent on the simulated network. Compared with a standard NMA, the methodology increased the statistical efficiency of the indirect treatment comparison (ITC). The use of the method was further illustrated on a dataset consisting of seven randomized clinical trials (RCTs) (26 treatment arms) in the treatment of obesity with Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), with a broad range of doses and follow-up times. The method integrated phase 2 and 3 data seamlessly into the meta-analysis and provided greater precision on the treatment effects compared to NMA. Finally, the statistical framework may be used to support clinical decision-making, providing a framework for ITC during drug development.

Auditory hallucinations (AH) frequently persist in schizophrenia spectrum disorder despite antipsychotic treatment. Cognitive behavioral therapy (CBT) remains an established psychological intervention, whereas AVATAR (Audio Visual Assisted Therapy Aid for Refractory auditory hallucinations) therapy has recently been introduced as a novel approach integrating interactive digital avatars. This meta-analysis compared the efficacy of AVATAR therapy with CBT for medication-resistant AH. A systematic search of five major databases up to June 1, 2025 identified randomized controlled trials (RCTs) that evaluated either therapy. The primary outcome was AH severity, and secondary outcomes included psychotic symptoms, mood measures, and all-cause discontinuation. Twenty-six RCTs (n = 2273; 65.0% male; mean age 39.3 [SD 4.1] years) met inclusion criteria. Compared with CBT, AVATAR therapy showed no significantly greater reduction in AH severity (standardized mean difference [SMD] = −0.23, 95% confidence interval [CI] = −0.55 to 0.10). However, it demonstrated superior sustained improvement three months post-treatment (SMD = −0.37, 95% CI = −0.69 to −0.05) and greater reduction in overall psychotic symptoms (SMD = −0.41, 95% CI = −0.75 to −0.06). No significant differences were observed in positive, negative, depressive, anxiety, or quality-of-life outcomes, and discontinuation rates were comparable. Interpretation should be cautious given evidence of small-study effects (Egger’s tests p < 0.01 for AH severity) and predominantly moderate-to-high risk of bias across included trials. AVATAR therapy therefore shows lasting efficacy, comparable or slightly superior to CBT, and may serve as an alternative for patients with medication-resistant AH.

Network meta-analysis (NMA) provides a powerful framework for synthesizing evidence across multiple interventions, accommodating both direct and indirect comparisons. However, effectively visualizing the complex, multidimensional results, such as effect magnitudes, uncertainty, p-values, and treatment rankings, remains a significant challenge. Outputs such as relative treatment effects, uncertainty, statistical significance, and treatment rankings are often reported separately, making it difficult for researchers and stakeholders to synthesize findings efficiently. We introduce plate plot, an innovative approach for visualizing key outcomes from NMA in a single, compact format. It enables simultaneous display of point estimates, confidence or credible intervals, significance levels, and surface under the cumulative ranking curve values, thereby facilitating clearer interpretation and communication of NMA findings. Using an example dataset, we demonstrate how the plate plot displays multiple relevant metrics to compare the efficacy and acceptability of various antidepressant interventions in a single, intuitive plot. The plate plot, generated effortlessly via the open-source nmaplateplot R package, enables users to generate customizable, publication-ready graphics with minimal programming. This tool enhances the ability to holistically evaluate and interpret complex comparative effectiveness data, supporting better-informed decision-making in research and clinical practice.

Network meta-analysis (NMA) facilitates the comparison of multiple treatments by integrating both direct and indirect evidence. Applications of NMA in medical decision making have grown exponentially. However, the validity of NMA findings depends on key assumptions: homogeneity, transitivity, and consistency. A lack of consistent assessment of these assumptions potentially compromises the reliability of NMA outcomes. The objective of this study is to evaluate the extent to which researchers address NMA assumptions and report the assessment of evidence certainty in NMA publications. A total of 22,079 studies were identified from PubMed, Embase, and Cochrane CENTRAL (January 2010–August 2024). A sample of 393 NMAs was calculated to represent this population and randomly selected. Data on study characteristics, NMA assumptions, and the certainty of evidence were extracted and analyzed descriptively. Of the 393 NMAs, 71.8% were published between 2020 and 2024. Homogeneity was assessed in 300 (76.3%) NMAs, transitivity in 45 (11.5%) NMAs, and consistency in 265 (67.4%) NMAs. The certainty of evidence was assessed in 110 (28.0%) studies, predominantly using GRADE (71 NMAs; 18.1%) or CINeMA (29 NMAs; 7.4%). NMAs published in journals with high-impact factors more frequently evaluate these aspects than those published in low-impact journals. The assessment of NMA assumptions is inconsistently reported across studies, particularly for transitivity and consistency assumptions. Our findings highlight the need for standardized protocols or reporting guidelines to ensure these assessments are conducted and transparently reported.

In many areas of oncology, cancer drugs are now associated with long-term survivorship and mixture cure models (MCM) are increasingly being used for survival analysis. The objective of this article was to propose a methodology for conducting network meta-analysis (NMA) of MCM. This method was illustrated through a case study evaluating recurrence-free survival (RFS) with adjuvant therapy for stage III/IV resected melanoma. For the case study, the MCM NMA was conducted by: (1) fitting MCMs to each trial included within the network of evidence; and (2) incorporating the parameters of the MCMs into a multivariate NMA. Outputs included relative effect estimates for the MCM NMA as well as absolute estimates of survival (RFS), modeled within the Bayesian multivariate NMA, by incorporating absolute baseline effects of the reference treatment. The case study was intended for illustrative purposes of the MCM NMA methodology and is not meant for clinical interpretation. The case study demonstrated the feasibility of conducting an MCM NMA and highlighted key issues and considerations when conducting such analyses, including plausibility of cure, maturity of data, process for model selection, and the presentation and interpretation of results. MCM NMA provides a method of comparative survival that acknowledges the benefit newer treatments may confer on a subset of patients, resulting in long-term survival and reflection of this survival in extrapolation. In the future, this method may provide an additional metric to compare treatments that is of value to patients.

The contrast-based model (CBM) is the most popular network meta-analysis (NMA) method, although alternative approaches, e.g., the baseline model (BM), have been proposed but seldom used. This article aims to illuminate the difference between the CBM and BM and explores when they produce different results. These models differ in key assumptions: The CBM assumes treatment contrasts are exchangeable across trials and models the reference (baseline) treatment’s outcome levels as fixed effects, while the BM further assumes that the baseline treatment’s outcome levels are exchangeable across trials and treats them as random effects. We show algebraically and graphically that the difference between the CBM and BM is analogous to the difference between the two analyses in a statistical conundrum called Lord’s Paradox, in which the t-test and analysis of covariance (ANCOVA) yield conflicting conclusions about the group difference in weight gain. We show that this conflict arises because the t-test compares the observed weight change, whereas ANCOVA compares an adjusted weight change. In NMA, analogously, the CBM compares observed treatment contrasts, while the BM compares adjusted treatment contrasts. We demonstrate how the difference in modeling baseline effects can cause the CBM and BM to give different results. The analogy of Lord’s Paradox provides insights into the different assumptions of the CBM and BM regarding the relationship between baseline effects and treatment contrasts. When these two models produce substantially different results, it may indicate a violation of the transitivity assumption. Therefore, we should be cautious in interpreting the results from either model.

For network meta-analysis (NMA), we usually assume that the treatment arms are independent within each included trial. This assumption is justified for parallel design trials and leads to a property we call consistency of variances for both multi-arm trials and NMA estimates. However, the assumption is violated for trials with correlated arms, for example, split-body trials. For multi-arm trials with correlated arms, the variance of a contrast is not the sum of the arm-based variances, but comes with a correlation term. This may lead to violations of variance consistency, and the inconsistency of variances may even propagate to the NMA estimates. We explain this using a geometric analogy where three-arm trials correspond to triangles and four-arm trials correspond to tetrahedrons. We also investigate which information has to be extracted for a multi-arm trial with correlated arms and provide an algorithm to analyze NMAs including such trials.

Network meta-analysis (NMA) is becoming increasingly important, especially in the field of medicine, as it allows for comparisons across multiple trials with different interventions. For time-to-event data, that is, survival data, traditional NMA based on the proportional hazards (PH) assumption simply synthesizes reported hazard ratios (HRs). Novel methods for NMA based on the non-PH assumption have been proposed and implemented using R software. However, these methods often involve complex methodologies and require advanced programming skills, creating a barrier for many researchers. Therefore, we developed an R Shiny tool, NMAsurv (https://psurvivala.shinyapps.io/NMAsurv/). NMAsurv allows users with little or zero background in R to conduct survival-data-based NMA effortlessly. The tool supports various functions such as drawing network plots, testing the PH assumption, and building NMA models. Users can input either reconstructed pseudo-individual participant data or aggregated data. NMAsurv offers a user-friendly interface for extracting parameter estimations from various NMA models, including fractional polynomial, piecewise exponential models, parametric survival models, Cox PH model, and generalized gamma model. Additionally, it enables users to effortlessly create survival and HR plots. All operations can be performed by an intuitive “point-and-click” interface. In this study, we introduce all the functionalities and features of NMAsurv and demonstrate its application using a real-world NMA example.

Network meta-analysis (NMA) enables simultaneous assessment of multiple treatments by combining both direct and indirect evidence. While NMAs are increasingly important in healthcare decision-making, challenges remain due to limited direct comparisons between treatments. This data sparsity complicates the accurate estimation of correlations among treatments in arm-based NMA (AB-NMA). To address these challenges, we introduce a novel sensitivity analysis tool tailored for AB-NMA. This study pioneers a tipping point analysis within a Bayesian framework, specifically targeting correlation parameters to assess their influence on the robustness of conclusions about relative treatment effects. The analysis explores changes in the conclusion based on whether the 95% credible interval includes the null value (referred to as the interval conclusion) and the magnitude of point estimates. Applying this approach to multiple NMA datasets, including 112 treatment pairs, we identified tipping points in 13 pairs (11.6%) for interval conclusion change and in 29 pairs (25.9%) for magnitude change with a threshold at 15%. These findings underscore potential commonality in tipping points and emphasize the importance of our proposed analysis, especially in networks with sparse direct comparisons or wide credible intervals for correlation estimates. A case study provides a visual illustration and interpretation of the tipping point analysis. We recommend integrating this tipping point analysis as a standard practice in AB-NMA.

The importance of network meta-analysis (NMA) methods for time-to-event (TTE) that do not rely on the proportional hazard (PH) assumption is increasingly recognized in oncology, where clinical trials evaluating new interventions versus standard comparators often violate this assumption. However, existing NMA methods that allow for time-varying treatment effects do not directly leverage individual events and censor times that can be reconstructed from Kaplan–Meier curves, which may be more accurate than discrete hazards. They are also challenging to implement given reparameterizations that rely on discrete hazards. Additionally, two-step methods require assumptions regarding within-study normality and variance. We propose a one-step fully Bayesian parametric individual patient data (IPD)-NMA model that fits TTE data with the exact likelihood and allows for time-varying treatment effects. We define fixed or random effects with the following distributions: Weibull, Gompertz, log-normal, log-logistic, gamma, or generalized gamma distributions. We apply the one-step model to a network of randomized controlled trials (RCTs) evaluating multiple interventions for advanced melanoma and compare results with those obtained with the two-step approach. Additionally, a simulation study was performed to compare the proposed one-step method to the two-step method. The one-step method allows for straightforward model selection among the “standard” distributions, now including gamma and generalized gamma, with treatment effects on either the scale alone or with multivariate treatment effects. Generalized gamma offers flexibility to model U-shaped hazards within a network of RCTs, with accessible interpretation of parameters that simplifies to exponential, Weibull, log-normal, or gamma in special cases.

Network meta-analysis allows the synthesis of relative effects from several treatments. Two broad approaches are available to synthesize the data: arm-synthesis and contrast-synthesis, with several models that can be fitted within each. Limited evaluations comparing these approaches are available. We re-analyzed 118 networks of interventions with binary outcomes using three contrast-synthesis models (CSM; one fitted in a frequentist framework and two in a Bayesian framework) and two arm-synthesis models (ASM; both fitted in a Bayesian framework). We compared the estimated log odds ratios, their standard errors, ranking measures and the between-trial heterogeneity using the different models and investigated if differences in the results were modified by network characteristics. In general, we observed good agreement with respect to the odds ratios, their standard errors and the ranking metrics between the two Bayesian CSMs. However, differences were observed when comparing the frequentist CSM and the ASMs to each other and to the Bayesian CSMs. The network characteristics that we investigated, which represented the connectedness of the networks and rareness of events, were associated with the differences observed between models, but no single factor was associated with the differences across all of the metrics. In conclusion, we found that different models used to synthesize evidence in a network meta-analysis (NMA) can yield different estimates of odds ratios and standard errors that can impact the final ranking of the treatment options compared.

The treatment recommendation based on a network meta-analysis (NMA) is usually the single treatment with the highest expected value (EV) on an evaluative function. We explore approaches that recommend multiple treatments and that penalise uncertainty, making them suitable for risk-averse decision-makers. We introduce loss-adjusted EV (LaEV) and compare it to GRADE and three probability-based rankings. We define properties of a valid ranking under uncertainty and other desirable properties of ranking systems. A two-stage process is proposed: the first identifies treatments superior to the reference treatment; the second identifies those that are also within a minimal clinically important difference (MCID) of the best treatment. Decision rules and ranking systems are compared on stylised examples and 10 NMAs used in NICE (National Institute of Health and Care Excellence) guidelines. Only LaEV reliably delivers valid rankings under uncertainty and has all the desirable properties. In 10 NMAs comparing between 5 and 41 treatments, an EV decision maker would recommend 4–14 treatments, and LaEV 0–3 (median 2) fewer. GRADE rules give rise to anomalies, and, like the probability-based rankings, the number of treatments recommended depends on arbitrary probability cutoffs. Among treatments that are superior to the reference, GRADE privileges the more uncertain ones, and in 3/10 cases, GRADE failed to recommend the treatment with the highest EV and LaEV. A two-stage approach based on MCID ensures that EV- and LaEV-based rules recommend a clinically appropriate number of treatments. For a risk-averse decision maker, LaEV is conservative, simple to implement, and has an independent theoretical foundation.