A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis

Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons

Effects of study precision and risk of bias in networks of interventions: a network meta-epidemiological study

Background Empirical research has illustrated an association between study size and relative treatment effects, but conclusions have been inconsistent about the association of study size with the risk of bias items. Small studies give generally imprecisely estimated treatment effects, and study variance can serve as a surrogate for study size. Methods We conducted a network meta-epidemiological study analyzing 32 networks including 613 randomized controlled trials, and used Bayesian network meta-analysis and meta-regression models to evaluate the impact of trial characteristics and study variance on the results of network meta-analysis. We examined changes in relative effects and between-studies variation in network meta-regression models as a function of the variance of the observed effect size and indicators for the adequacy of each risk of bias item. Adjustment was performed both within and across networks, allowing for between-networks variability. Results Imprecise studies with large variances tended to exaggerate the effects of the active or new intervention in the majority of networks, with a ratio of odds ratios of 1.83 (95% CI: 1.09,3.32). Inappropriate or unclear conduct of random sequence generation and allocation concealment, as well as lack of blinding of patients and outcome assessors, did not materially impact on the summary results. Imprecise studies also appeared to be more prone to inadequate conduct. Conclusions Compared to more precise studies, studies with large variance may give substantially different answers that alter the results of network meta-analyses for dichotomous outcome

Evaluating the quality of evidence from a network meta-analysis

Systematic reviews that collate data about the relative effects of multiple interventions via network meta-analysis are highly informative for decision-making purposes. A network meta-analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a network meta-analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a network meta-analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the network meta-analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of network meta-analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a network meta-analysis. Judgements about evidence from a network meta-analysis can be different from those made about evidence from pairwise meta-analyses. © 2014 Salanti et al

Sharing information across patient subgroups to draw conclusions from sparse treatment networks

Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large sample approximations which are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks

CINeMA: An approach for assessing confidence in the results of a network meta-analysis.

BACKGROUND The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. METHODOLOGY CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. CONCLUSIONS Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group

Comparative effects of different dietary approaches on blood pressure in hypertensive and pre-hypertensive patients: A systematic review and network meta-analysis

Pairwise meta-analyses have shown beneficial effects of individual dietary approaches on blood pressure but their comparative effects have not been established. Objective: Therefore we performed a systematic review of different dietary intervention trials and estimated the aggregate blood pressure effects through network meta-analysis including hypertensive and pre-hypertensive patients. Design: PubMed, Cochrane CENTRAL, and Google Scholar were searched until June 2017. The inclusion criteria were defined as follows: i) Randomized trial with a dietary approach; ii) hypertensive and pre-hypertensive adult patients; and iii) minimum intervention period of 12 weeks. In order to determine the pooled effect of each intervention relative to each of the other intervention for both diastolic and systolic blood pressure (SBP and DBP), random effects network meta-analysis was performed. Results: A total of 67 trials comparing 13 dietary approaches (DASH, lowfat, moderate-carbohydrate, high-protein, low-carbohydrate, Mediterranean, Palaeolithic, vegetarian, low-GI/GL, low-sodium, Nordic, Tibetan, and control) enrolling 17,230 participants were included. In the network metaanalysis, the DASH, Mediterranean, low-carbohydrate, Palaeolithic, high-protein, low-glycaemic index, lowsodium, and low-fat dietary approaches were significantly more effective in reducing SBP (¡8.73 to ¡2.32 mmHg) and DBP (¡4.85 to ¡1.27 mmHg) compared to a control diet. According to the SUCRAs, the DASH diet was ranked the most effective dietary approach in reducing SBP (90%) and DBP (91%), followed by the Palaeolithic, and the low-carbohydrate diet (ranked 3rd for SBP) or the Mediterranean diet (ranked 3rd for DBP). For most comparisons, the credibility of evidence was rated very low to moderate, with the exception for the DASH vs. the low-fat dietary approach for which the quality of evidence was rated high. Conclusion: The present network meta-analysis suggests that the DASH dietary approach might be the most effective dietary measure toreduce blood pressure among hypertensive and pre-hypertensive patients based on high quality evidence

Psychological and psychosocial interventions for treatment-resistant schizophrenia:a systematic review and network meta-analysis

BACKGROUND: Many patients with schizophrenia have symptoms that do not respond to antipsychotics. This condition is called treatment-resistant schizophrenia and has not received specific attention as opposed to general schizophrenia. Psychological and psychosocial interventions as an add-on treatment to pharmacotherapy could be useful, but their role and comparative efficacy to each other and to standard care in this population are not known. We investigated the efficacy, acceptability, and tolerability of psychological and psychosocial interventions for patients with treatment-resistant schizophrenia.METHODS: In this systematic review and network meta-analysis (NMA), we searched for published and unpublished randomised controlled trials (RCTs) through a systematic database search in BIOSIS, CINAHL, Embase, LILACS, MEDLINE, PsychInfo, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform for articles published from inception up to Jan 31, 2020. We also searched the Cochrane Schizophrenia Group registry for studies published from inception up to March 31, 2022, and PubMed and Cochrane CENTRAL for studies published from inception up to July 31, 2023. We included RCTs that included patients with treatment-resistant schizophrenia. The primary outcome was overall symptoms. We did random-effects pairwise meta-analyses and NMAs to calculate standardised mean differences (SMDs) or risk ratios with 95% CIs. No people with lived experience were involved throughout the research process. The study protocol was registered in PROSPERO, CRD42022358696.FINDINGS: We identified 30 326 records, excluding 24 526 by title and abstract screening. 5762 full-text articles were assessed for eligibility, of which 5540 were excluded for not meeting the eligibility criteria, and 222 reports corresponding to 60 studies were included in the qualitative synthesis. Of these, 52 RCTs with 5034 participants (1654 [33·2%] females and 3325 [66·8%] males with sex indicated) comparing 20 psychological and psychosocial interventions provided data for the NMA. Mean age of participants was 38·05 years (range 23·10-48·50). We aimed to collect ethnicity data, but they were scarcely reported. According to the quality of evidence, cognitive behavioural therapy for psychosis (CBTp; SMD -0·22, 95% CI -0·35 to -0·09, 35 trials), virtual reality intervention (SMD -0·41, -0·79 to -0·02, four trials), integrated intervention (SMD -0·70, -1·18 to -0·22, three trials), and music therapy (SMD -1·27, -1·83 to -0·70, one study) were more efficacious than standard care in reducing overall symptoms. No indication of publication bias was identified.INTERPRETATION: We provide robust findings that CBTp can reduce the overall symptoms of patients with treatment-resistant schizophrenia, and therefore clinicians can prioritise this intervention in their clinical practice. Other psychological and psychosocial interventions showed promising results but need further investigation.FUNDING: DAAD-ASFE.</p