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Objectives To establish current guidance and practice in UK on presentation of indirect comparison and mixed treatment comparison analyses; to provide recommendations to improve indirect comparison/mixed treatment comparison reporting and to identify research priorities for improved presentation. Methods Existing institutional guidance for conducting indirect comparison/mixed treatment comparison alongside current practice in health technology assessment was reviewed. Reports published in UK by the Health Technology Assessment programme since 1997, which utilized indirect comparison/mixed treatment comparison methods, were reviewed with respect to the presentation of study data, statistical models and results. Recommendations for presentation were developed. Results Guidance exists that provide the details necessary to conduct a successful indirect comparison/mixed treatment comparison analysis but recommendations on presentation are limited. Of 205 health technology assessment reports that contained evidence synthesis for effectiveness, 19 used indirect comparison/mixed treatment comparison methods. These reports utilized numerous presentational formats from which the following key components were identified: network table/diagram for presenting data; model description to allow reproducibility; and tables, forest plots, matrix tables and summary forest plots for presenting a range of results. Recommendations were developed to ensure that reporting is explicit, transparent and reproducible. Approaches most understandable by non-technical decision makers, and areas where future research is required, are outlined. Conclusions There is no standard presentational style used in UK for reporting indirect comparison/mixed treatment comparison, and the use of graphical tools is limited. Standardization of reporting and innovation in graphical representation of indirect comparison/mixed treatment comparison results is required.

Objectives:The aim of this study was to examine the use of implicit and explicit Bayesian methods in health technology assessments and to identify whether this has changed over time.Methods:A review of all health technology assessment (HTA) reports of secondary research published by the UK National Institute of Health Research (NIHR) between 1997 and 2011. Data were extracted on the use and implementation of Bayesian methods, whether defined as such by the original authors (i.e., explicit) or not (i.e., implicit).Results: A total of 155 of 375 (41 percent) NIHR HTA reports, identified as relevant to this review, contained a Bayesian analysis. Of these, 128 (83 percent) contained an implicit Bayesian analysis, 3 (2 percent) an explicit Bayesian analysis and 24 (15 percent) both implicit and explicit Bayesian analyses. Of the twenty-seven reports that explicitly used Bayes theorem, only six included prior information in the form of (informative) prior distributions. Over time, the percentage of HTA reports that used Bayesian (implicit and/or explicit) methods increased from 0 percent in 1997 to nearly 80 percent in 2011.Conclusions: This review has shown that there has been an increase in the use of Bayesian methods in HTA, which is likely to be a result of the increase in freely available resources to implement the approach. Areas where Bayesian methods have the potential to advance healthcare evaluations in the future are considered in the discussion.

The usefulness of a multivariate approach to compare treatments in the context of pairwise meta-analysis has been widely demonstrated in the literature. However, this approach has not yet been considered for multiple treatment comparisons. We believe that extending such methodology to network meta-analysis (NMA) will increase the primary evidence base allowing us to compare more interventions across multiple outcomes measures. Borrowing strength between outcome measures using multivariate NMA can also potentially increase the precision of relative treatment effect estimates and reduce the impact of outcome reporting bias. Objectives To extend standard NMA to incorporate multiple outcomes of interest and evaluate the use of multivariate NMA models through simulated and real datasets. Methods We developed a random effects multivariate NMA model to account for the correlation between multiple outcome measures. The potential benefits of this method were demonstrated in a simulated example comparing univariate and bivariate NMAs for continuous outcome measures. We further explored the application of our multivariate NMA model using a case study comparing anti-obesity pharmacological interventions for waist circumference, weight change and BMI change from baseline. Results The simulation study showed that through use of multivariate NMA the precision in mean relative treatment effects increased compared to a standard univariate NMA. This held true under multiple scenarios testing model parameters including both within- and between-outcome correlations. Similar findings were obtained from the application to the example dataset in obesity. Conclusions Our method proves particularly useful in reducing uncertainty around relative effectiveness estimates when the outcomes included for analysis are highly correlated. However, the advantages of the multivariate NMA are limited where there is little correlation between outcome measures. Further work will explore the applicability of multivariate NMA methods to different types of outcomes such as binary outcome measures.

8580 Background: Ipilimumab is the first treatment to show an OS benefit in a phase III trial (MDX010-20) compared with a gp100 vaccine. The majority of ipilimumab-associated adverse events were me...