Meta-analysis of the prevalence of attention-deficit hyperactivity disorder in prison: A comment on Fazel and Favril (2024) and reanalysis of the data.

BACKGROUND Fazel and Favril presented a reanalysis of our previously published systematic review and meta-analysis on the prevalence of attention deficit hyperactivity disorder (ADHD) in prison. AIMS The current paper addresses some of the criticisms of Fazel and Favril on our meta-analysis and presents a reanalysis of the data, focusing on adult detained persons. METHODS We conducted a meta-regression on 28 studies (n = 7710) to estimae the pooled prevalence of ADHD. RESULTS This reanalysis yielded a pooled estimate of 22.2% for the prevalence of ADHD (95% confidence interval [CI]: 15.7; 28.6), which disagrees with the estimate given by Fazel and Favril (8.3%, 95% CI: 3.8; 12.8). CONCLUSION We argue that the ADHD prevalence provided by Fazel and Favril was an underestimate due to their use of too restrictive exclusion criteria and suboptimal analysis methods. Our reanalysis on detained adults suggests a higher ADHD prevalence, which highlights the need to diagnose and treat ADHD in prison

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

The impact of continuity correction methods in Cochrane reviews with single-zero trials with rare events: A meta-epidemiological study.

Meta-analyses examining dichotomous outcomes often include single-zero studies, where no events occur in intervention or control groups. These pose challenges, and several methods have been proposed to address them. A fixed continuity correction method has been shown to bias estimates, but it is frequently used because sometimes software (e.g., RevMan software in Cochrane reviews) uses it as a default. We aimed to empirically compare results using the continuity correction with those using alternative models that do not require correction. To this aim, we reanalyzed the original data from 885 meta-analyses in Cochrane reviews using the following methods: (i) Mantel-Haenszel model with a fixed continuity correction, (ii) random effects inverse variance model with a fixed continuity correction, (iii) Peto method (the three models available in RevMan), (iv) random effects inverse variance model with the treatment arm continuity correction, (v) Mantel-Haenszel model without correction, (vi) logistic regression, and (vii) a Bayesian random effects model with binominal likelihood. For each meta-analysis we calculated ratios of odds ratios between all methods, to assess how the choice of method may impact results. Ratios of odds ratios <0.8 or <1.25 were seen in ~30% of the existing meta-analyses when comparing results between Mantel-Haenszel model with a fixed continuity correction and either Mantel-Haenszel model without correction or logistic regression. We concluded that injudicious use of the fixed continuity correction in existing Cochrane reviews may have substantially influenced effect estimates in some cases. Future updates of RevMan should incorporate less biased statistical methods

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

Efficacy of pharmacological interventions for ADHD: protocol for an updated systematic review and dose–response network meta-analysis

: Background: Attention-deficit/hyperactivity disorder (ADHD) affects approximately 5% of children globally, with symptoms often persisting into adulthood. While pharmacological interventions are commonly employed for management, understanding the optimal dosing for efficacy and tolerability remains crucial. This study aims to conduct a dose–response network meta-analysis to estimate the efficacy of pharmacological treatments across different doses, aiming to inform clinical decision-making and improve treatment outcomes. Methods: This updated systematic review will include randomized controlled trials evaluating ADHD medication efficacy in children, adolescents, and adults. An updated search from a 2018 NMA will be conducted across multiple electronic databases with no language restrictions, using specific eligibility criteria focused on randomized controlled trials. The primary outcome will assess the severity of ADHD core symptoms, while secondary outcomes will consider treatment tolerability. A dose–response Bayesian hierarchical model will be used to estimate dose–response curves for each medication, identifying optimal dosing strategies. Discussion: With this dose–response network meta-analysis, we aim to better understand the dose–response relationship of pharmacological treatment in ADHD, which could help clinician to the identification of optimal doses. Systematic review registration: OSF https://doi.org/10.17605/OSF.IO/3MY4A

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

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