Inconsistent Results for Peto Odds Ratios in Multi-Arm Studies, Network Meta-Analysis and Indirect Comparisons.

The Peto odds ratio is a well known effect measure in meta-analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in the situation of unbalanced sample sizes in the two treatment groups or large treatment effects. In this publication, we evaluate Peto odds ratio estimators in the setting of multi-armstudies and in network meta-analysis using illustrative examples. We observe that Peto odds ratio estimators in a multi-arm study are inconsistent if the observed event probabilities are different or the sample sizes of treatment groups are unbalanced. The same problem emerges in network meta-analysis including only two-arm studies and translates to indirect comparisons of pairwise meta-analyses. We conclude that the Peto odds ratio should not be used as effect measure in network meta-analysis or indirect comparisons of pairwise meta-analyses. This article is protected by copyright. All rights reserved

Network meta-analysis of rare events using the Mantel-Haenszel method

The Mantel-Haenszel (MH) method has been used for decades to synthesize data obtained from studies that compare two interventions with respect to a binary outcome. It has been shown to perform better than the inverse-variance method or Peto's odds ratio when data is sparse. Network meta-analysis (NMA) is increasingly used to compare the safety of medical interventions, synthesizing, eg, data on mortality or serious adverse events. In this setting, sparse data occur often and yet there is to-date, no extension of the MH method for the case of NMA. In this paper, we fill this gap by presenting a MH-NMA method for odds ratios. Similarly to the pairwise MH method, we assume common treatment effects. We implement our approach in R, and we provide freely available easy-to-use routines. We illustrate our approach using data from two previously published networks. We compare our results to those obtained from three other approaches to NMA, namely, NMA with noncentral hypergeometric likelihood, an inverse-variance NMA, and a Bayesian NMA with a binomial likelihood. We also perform simulations to assess the performance of our method and compare it with alternative methods. We conclude that our MH-NMA method offers a reliable approach to the NMA of binary outcomes, especially in the case or sparse data, and when the assumption of methodological and clinical homogeneity is justifiable

Introducing the Treatment Hierarchy Question in Network Meta-Analysis

Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the most to the least preferable option. However, in published reviews, the research question associated with the hierarchy of multiple interventions is typically not clearly defined. Here we introduce the novel 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. We discuss the most commonly used ranking metrics (quantities that compare the estimated treatment-specific effects), how the ranking metrics produce a treatment hierarchy, and the type of treatment hierarchy question that each ranking 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. When using network meta-analyses that aim to rank treatments, investigators should state the treatment hierarchy question they aim to address and employ the appropriate ranking metric to answer it. Following this new proposal will avoid some controversies that have arisen in comparative effectiveness research

netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods

Network meta-analysis compares different interventions for the same condition, by combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major research topic for methodologists. This article describes the R package netmeta, which adopts frequentist methods to fit network meta-analysis models. We provide a roadmap to perform network meta-analysis, along with an overview of the main functions of the package. We present three worked examples considering different types of outcomes and different data formats to facilitate researchers aiming to conduct network meta-analysis with netmeta

A systematic review and meta-analysis investigating the relationship between metabolic syndrome and the incidence of thyroid diseases.

PURPOSE To assess the prospective association between metabolic syndrome (MetS), its components, and incidence of thyroid disorders by conducting a systematic review and meta-analysis. METHODS A systematic search was performed in Ovid Medline, Embase.com, and Cochrane CENTRAL from inception to February 22, 2023. Publications from prospective studies were included if they provided data on baseline MetS status or one of its components and assessed the incidence of thyroid disorders over time. A random effects meta-analysis was conducted to calculate the odds ratio (OR) for developing thyroid disorders. RESULTS After full-text screening of 2927 articles, seven studies met our inclusion criteria. Two of these studies assessed MetS as an exposure (N = 71,727) and were included in our meta-analysis. The association between MetS at baseline and incidence of overt hypothyroidism at follow-up yielded an OR of 0.78 (95% confidence interval [CI]: 0.52-1.16 for two studies, I2 = 0%). Pooled analysis was not possible for subclinical hypothyroidism, due to large heterogeneity (I2 = 92.3%), nor for hyperthyroidism, as only one study assessed this association. We found evidence of an increased risk of overt (RR: 3.10 (1.56-4.64, I2 = 0%) and subclinical hypothyroidism (RR 1.50 (1.05-1.94), I2 = 0%) in individuals with obesity at baseline. There was a lower odds of developing overt hyperthyroidism in individuals with prediabetes at baseline (OR: 0.68 (0.47-0.98), I2 = 0%). CONCLUSIONS We were unable to draw firm conclusions regarding the association between MetS and the incidence of thyroid disorders due to the limited number of available studies and the presence of important heterogeneity in reporting results. However, we did find an association between obesity at baseline and incidence of overt and subclinical hypothyroidism

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions.

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain

Relative effectiveness of non-surgical interventions for pain management in knee osteoarthritis: a protocol for a component network meta-analysis of randomised controlled trials

Introduction: Knee osteoarthritis is a chronic degenerative disease associated with significant chronic pain, disability and impaired quality of life and is the most common form of osteoarthritis. There is no cure for knee osteoarthritis, and the main therapeutic goals are pain management and improving quality of life. The objective of this study is to evaluate the relative efficacy and acceptability of available interventions using network meta-analysis (NMA) to provide a comprehensive evidence base to inform future treatment guidelines. Methods and Analysis: A comprehensive literature search of major electronic databases (MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials) and clinical trial registries will identify randomised control trials (RCTs) of interventions listed in NICE guidelines for the treatment of knee osteoarthritis in adults. We will perform an NMA to estimate relative intervention effects across the whole treatment network. If any studies use multicomponent intervention packages, we will employ a component NMA model to estimate the contribution of individual components. The quality of evidence will be assessed using the Confidence in Network Meta-Analysis approach, which is based on the traditional GRADE framework adapted for NMA. Risk of bias (RoB) will be assessed using the revised Cochrane RoB 2.0 tool for RCTs

Real-world effect of antidepressants for depressive disorder in primary care: protocol of a population-based cohort study

Introduction: Clinical guidelines recommend antidepressants as the first line of treatment for adults with moderate-to-severe depression. Randomised trials provide the best evidence on the comparative effectiveness of antidepressants for depression, but are limited by a short follow-up and a highly selected population. We aim to conduct a cohort study on a large database to assess acceptability, efficacy, safety and tolerability of antidepressant monotherapy in people with depressive disorder in primary care.Methods and analysis: This is a protocol for a cohort study using data from the QResearch primary care research database, which is the largest general practice research database in the UK. We will include patients registered for at least 1 year from 1 January 1998, diagnosed with a new episode of depression and on antidepressant and a comparison group not on antidepressant. The exposure of interest will be treatment with antidepressant medications. Our outcomes will be acceptability (treatment discontinuation due to any cause), efficacy (clinical response and remission); safety (adverse events (AEs) and all-cause mortality); and tolerability (dropouts due to any AE) measured at 2 months, 6 months and 1 year. For each outcome, we will estimate the absolute risks for all antidepressants, and relative effects between antidepressants using Cox’s proportion hazards models. We will calculate HRs and 99.9% CIs for each outcome of interest.Discussion: The main limitation is the observational nature of our study, while the major strengths include the large representative population contained in QResearch and the possibly high generalisability

Measuring the performance of prediction models to personalize treatment choice.

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect