Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples.
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ABSTRACT:
SUBMITTER: Riley RD
PROVIDER: S-EPMC5596393 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature
Publications
Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples.
Riley Richard D RD Jackson Dan D Salanti Georgia G Burke Danielle L DL Price Malcolm M Kirkham Jamie J White Ian R IR
BMJ (Clinical research ed.) 20170913
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Project description:BackgroundModel-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to time-course models.MethodsWe propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis.ResultsOf the time-course functions that we explored, the Emax model gave the best fit to the data and has biological plausibility. Some simplifying assumptions were needed to identify the ET50 , due to few observations at early follow-up times. Treatment estimates were robust to the inclusion of correlations in the likelihood.ConclusionsTime-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.
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Project description:Multivariate meta-analysis allows the joint synthesis of effect estimates based on multiple outcomes from multiple studies, accounting for the potential correlations among them. However, standard methods for multivariate meta-analysis for multiple outcomes are restricted to problems where the within-study correlation is known or where individual participant data are available. This paper proposes an approach to approximating the within-study covariances based on information about likely correlations between underlying outcomes. We developed methods for both continuous and dichotomous data and for combinations of the two types. An application to a meta-analysis of treatments for stroke illustrates the use of the approximated covariance in multivariate meta-analysis with correlated outcomes.
| S-EPMC3618374 | biostudies-literature
Project description:In this work, we propose a method for individualized treatment selection when there are correlated multiple responses for the K treatment ( K≥2 ) scenario. Here we use ranks of quantiles of outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables using any number of quantiles and it can be applied for a broad set of models. We propose a rank aggregation technique for combining several lists of ranks where both these lists and elements within each list can be correlated. The method has the flexibility to incorporate patient and clinician preferences into the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present illustrations using two different datasets from diabetes and HIV-1 clinical trials to show the applicability of the proposed procedure for real data.
| S-EPMC9232994 | biostudies-literature
Project description:Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.
| S-EPMC8497908 | biostudies-literature
Project description:BackgroundNetwork meta-analysis synthesises data from a number of clinical trials in order to assess the comparative efficacy of multiple healthcare interventions in similar patient populations. In situations where clinical trial data are heterogeneously reported i.e. data are missing for one or more outcomes of interest, synthesising such data can lead to disconnected networks of evidence, increased uncertainty, and potentially biased estimates which can have severe implications for decision-making. To overcome this issue, strength can be borrowed between outcomes of interest in multivariate network meta-analyses. Furthermore, in situations where there are relatively few trials informing each treatment comparison, there is a potential issue with the sparsity of data in the treatment networks, which can lead to substantial parameter uncertainty. A multivariate network meta-analysis approach can be further extended to borrow strength between interventions of the same class using hierarchical models.MethodsWe extend the trivariate network meta-analysis model to incorporate the exchangeability between treatment effects belonging to the same class of intervention to increase precision in treatment effect estimates. We further incorporate a missing data framework to estimate uncertainty in trials that did not report measures of variability in order to maximise the use of all available information for healthcare decision-making. The methods are applied to a motivating dataset in overactive bladder syndrome. The outcomes of interest were mean change from baseline in incontinence, voiding and urgency episodes. All models were fitted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS.ResultsAll models (univariate, multivariate, and multivariate models incorporating class effects) produced similar point estimates for all treatment effects. Incorporating class effects in multivariate models often increased precision in treatment effect estimates.ConclusionsMultivariate network meta-analysis incorporating class effects allowed for the comparison of all interventions across all outcome measures to ameliorate the potential impact of outcome reporting bias, and further borrowed strength between interventions belonging to the same class of treatment to increase the precision in treatment effect estimates for healthcare policy and decision-making.
| S-EPMC7341581 | biostudies-literature
Project description:In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually report different subsets of factors, and the results from separate analyses on multiple factors may not be comparable because each analysis may use different subpopulation. This may impact on selecting most important factors to design a multifactor intervention program. This article proposes a new concept, multivariate meta-analysis of multiple factors (MVMA-MF), to synthesize all available factors simultaneously. By borrowing information across factors, MVMA-MF can improve statistical efficiency and reduce biases compared with separate analyses when factors were missing not at random. As within-study correlations between factors are commonly unavailable from published articles, we use a Bayesian hybrid model to perform MVMA-MF, which effectively accounts for both within- and between-study correlations. The performance of MVMA-MF and the conventional methods are compared using simulations and an application to a pterygium dataset consisting of 29 studies on 8 risk factors.
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Project description:BackgroundMany onychomycosis treatments have not been directly compared in head-to-head clinical trials.ObjectiveTo determine the relative efficacy of onychomycosis treatments using network meta-analysis (NMA).MethodsWe conducted a systematic review and NMA of mycological cure rates.ResultsNineteen trials were included in the network. Terbinafine 250 mg was significantly superior to all treatments except itraconazole 400 mg pulse therapy. The itraconazole 400 mg pulse regimen was significantly superior to all topicals except efinaconazole 10% nail solution. Itraconazole 200 mg was significantly superior to all topical treatments, while fluconazole 150-450 mg, efinaconazole 10% nail solution, tavaborole 5% nail solution, ciclopirox nail lacquer 8%, terbinafine nail solution, and amorolfine 5% nail lacquer were significantly superior to placebo.ConclusionsNewly developed topicals have improved the odds ratios (ORs) of mycological cure, yet these ORs were not significantly greater than preexisting topical treatments. Further experience with these agents will reveal their clinical significance, and head-to-head trials are warranted. © 2015 S. Karger AG, Basel.
| S-EPMC4857825 | biostudies-literature
Project description:ObjectivesDental research typically targets multiple outcomes. Interdental cleaning devices such as interdental brushes (IB) and water jet devices (WJ) share a sizable portion of the medical device market. However, recommendations for device selection are limited by the conflicting evidence from multiple outcomes in available studies and the lack of an appropriate synthesis approach to summarize evidences taken from multiple outcomes. In particular, both pairwise meta-analyses and single-outcome network meta-analyses can give discordant results. The purpose of this multioutcome, Bayesian network meta-analysis is to introduce this innovative method to the dental research community using data from interdental cleaning device studies for illustrative purposes.MethodsWe reanalyzed a network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation, which included 22 trials assessing 10 interproximal oral hygiene aids. We focused on the primary outcome of gingival inflammation, which was measured by 2 correlated outcome variables, the Gingival Index (GI) and bleeding on probing (BOP).ResultsIn our previous single-outcome analysis, we concluded that IB and WJ rank high for reducing gingival inflammation while toothpick and flossing rank last. In this multioutcome Bayesian network meta-analysis with equal weight on GI and BOP, the surface under the cumulative ranking curve was 0.87 for WJ and 0.85 for IB. WJ and IB remained ranked as the 2 best devices across different sets of weightings for the GI and BOP.ConclusionIn conclusion, multioutcome Bayesian network meta-analysis naturally takes the correlations among multiple outcomes into account, which in turn can provide more comprehensive evidence.
| S-EPMC7211186 | biostudies-literature
Project description:ObjectivesMultivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful.Study design and settingWe use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes.ResultsFour characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model.ConclusionsCochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.
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Project description: Not available
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