Evidence Synthesis for Decision Making 6:Embedding Evidence Synthesis in Probabilistic Cost-effectiveness Analysis

When multiple parameters are estimated from the same synthesis model, it is likely that correlations will be induced between them. Network meta-analysis (mixed treatment comparisons) is one example where such correlations occur, along with meta-regression and syntheses involving multiple related outcomes. These correlations may affect the uncertainty in incremental net benefit when treatment options are compared in a probabilistic decision model, and it is therefore essential that methods are adopted that propagate the joint parameter uncertainty, including correlation structure, through the cost-effectiveness model. This tutorial paper sets out 4 generic approaches to evidence synthesis that are compatible with probabilistic cost-effectiveness analysis. The first is evidence synthesis by Bayesian posterior estimation and posterior sampling where other parameters of the cost-effectiveness model can be incorporated into the same software platform. Bayesian Markov chain Monte Carlo simulation methods with WinBUGS software are the most popular choice for this option. A second possibility is to conduct evidence synthesis by Bayesian posterior estimation and then export the posterior samples to another package where other parameters are generated and the cost-effectiveness model is evaluated. Frequentist methods of parameter estimation followed by forward Monte Carlo simulation from the maximum likelihood estimates and their variance-covariance matrix represent’a third approach. A fourth option is bootstrap resampling—a frequentist simulation approach to parameter uncertainty. This tutorial paper also provides guidance on how to identify situations in which no correlations exist and therefore simpler approaches can be adopted. Software suitable for transferring data between different packages, and software that provides a user-friendly interface for integrated software platforms, offering investigators a flexible way of examining alternative scenarios, are reviewed

Evidence Synthesis for Decision Making 2:A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials

We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pairwise meta-analysis, indirect comparisons, synthesis of multiarm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction. We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats

Evidence Synthesis for Decision Making 5:The Baseline Natural History Model

Most cost-effectiveness analyses consist of a baseline model that represents the absolute natural history under a standard treatment in a comparator set and a model for relative treatment effects. We review synthesis issues that arise on the construction of the baseline natural history model. We cover both the absolute response to treatment on the outcome measures on which comparative effectiveness is defined and the other elements of the natural history model, usually “downstream” of the shorter-term effects reported in trials. We recommend that the same framework be used to model the absolute effects of a “standard treatment” or placebo comparator as that used for synthesis of relative treatment effects and that the baseline model is constructed independently from the model for relative treatment effects, to ensure that the latter are not affected by assumptions made about the baseline. However, simultaneous modeling of baseline and treatment effects could have some advantages when evidence is very sparse or when other research or study designs give strong reasons for believing in a particular baseline model. The predictive distribution, rather than the fixed effect or random effects mean, should be used to represent the baseline to reflect the observed variation in baseline rates. Joint modeling of multiple baseline outcomes based on data from trials or combinations of trial and observational data is recommended where possible, as this is likely to make better use of available evidence, produce more robust results, and ensure that the model is internally coherent

Evidence Synthesis for Decision Making 1:Introduction

We introduce the series of 7 tutorial papers on evidence synthesis methods for decision making, based on the Technical Support Documents in Evidence Synthesis prepared for the National Institute for Health and Clinical Excellence (NICE) Decision Support Unit. Although oriented to NICE’s Technology Appraisal process, which examines new pharmaceutical products in a cost-effectiveness framework, the methods presented throughout the tutorials are equally relevant to clinical guideline development and to comparisons between medical devices, or public health interventions. Detailed guidance is given on how to use the other tutorials in the series, which propose a single evidence synthesis framework that covers fixed and random effects models, pairwise meta-analysis, indirect comparisons, and network meta-analysis, and where outcomes expressed in several different reporting formats can be analyzed without recourse to normal approximations. We describe the principles of evidence synthesis required by the 2008 revision of the NICE Guide to the Methods of Technology Appraisal and explain how the approach proposed in these tutorials was designed to conform to those requirements. We finish with some suggestions on how to present the evidence, the synthesis methods, and the results

Evidence Synthesis for Decision Making 3:Heterogeneity Subgroups, Meta-Regression, Bias, and Bias-Adjustment

In meta-analysis, between-study heterogeneity indicates the presence of effect-modifiers and has implications for the interpretation of results in cost-effectiveness analysis and decision making. A distinction is usually made between true variability in treatment effects due to variation in patient populations or settings and biases related to the way in which trials were conducted. Variability in relative treatment effects threatens the external validity of trial evidence and limits the ability to generalize from the results; imperfections in trial conduct represent threats to internal validity. We provide guidance on methods for meta-regression and bias-adjustment, in pairwise and network meta-analysis (including indirect comparisons), using illustrative examples. We argue that the predictive distribution of a treatment effect in a “new” trial may, in many cases, be more relevant to decision making than the distribution of the mean effect. Investigators should consider the relative contribution of true variability and random variation due to biases when considering their response to heterogeneity. In network meta-analyses, various types of meta-regression models are possible when trial-level effect-modifying covariates are present or suspected. We argue that a model with a single interaction term is the one most likely to be useful in a decision-making context. Illustrative examples of Bayesian meta-regression against a continuous covariate and meta-regression against “baseline” risk are provided. Annotated WinBUGS code is set out in an appendix

Satellite to Ground-based LIDAR Comparisons using MPLNET Data Products

The Micro-Pulse Lidar Network (MPLNET) is a network of ground-based lidar systems that provide continuous long-term observations of aerosol and cloud properties at approximately 10 different locations around the globe. Each site in the network uses an elastic scattering lidar co-located with a sunphotometer to provide data products of aerosol optical physical properties. Data products from sites are available on a next-day basis from the MPLNET website. Expansion of the network is based on partnering with research groups interested in joining MPLNET. Results have contributed to a variety of studies including aerosol transport studies and satellite calibration and validation efforts. One of the key motivations for MPLNET is to contribute towards the calibration and validation of satellite-based lidars such as GLAS/ICESAT and CALIPSO. MPLNET is able to provide comparison to several of the key aerosol and cloud CALIPSO data products including: layer height and thickness, optical depth, backscatter and extinction profiles, and the extinction-to-backscatter ratio

Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis

Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node-splitting analyses are labour-intensive because each comparison of interest requires a separate model. It would be advantageous if node-splitting models could be estimated automatically for all comparisons of interest. We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi-arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node-splitting approach, enabling the analyst to focus on interpreting the results. (C) 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd