Functional relationships in Bayesian evidence synthesis of multiple outcomes

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Healthcare policy and clinical decisions are usually based on a systemic review, critical appraisal and statistical summary of the evidence from previous clinical trials. It is hoped that this will provide information on which treatment or treatments should be recommended, based on how effective they are on a particular measure (for example that patients' pain improves by a greater amount). However, for many clinical conditions several different tools are used to assess patients' response to treatment. Treatment effects on each of these tools (outcomes) can also be reported in different formats. This is often the case in clinical guidelines produced for the UK's National Institute for Health and Care Excellence (NICE), where different, but often related, outcomes are considered equally important to guide recommendations for clinical practice. Performing separate analyses on each outcome, ignores the fact that outcomes are related and can lead to different or even conflicting conclusions. It is therefore desirable to summarise all the evidence on all outcomes in a coherent way. This makes best use of available evidence and will provide better (more precise) information on which to base clinical decisions and make policy recommendations. Current methods for summarising evidence on more than one outcome are technically difficult to implement and often do not greatly improve the precision of results. We propose more restrictive methods, which use expert opinion on how the various outcomes are related to obtain better estimates of treatment effects. For example, in the treatment of Social Anxiety, it is known that if patients' symptoms (measured on a particular scale) do not improve with treatment, then no patients will be classified as having recovered from the condition (measured on another scale). Conversely, treatments which improve symptoms are also more likely to have more patients achieving recovery. We propose methods for summarising the evidence on different, but related outcomes, by taking advantage of known relationships such as these, in order to obtain better treatment effect estimates.The proposed methods are technically simpler to implement than the methods currently proposed for summarising multiple outcomes. However, by imposing relationship between the treatment effects on the different outcomes, we will be making strong assumptions about the data. These assumptions will be checked using recommended statistical techniques and expert clinical opinion, to ensure that the proposed models are appropriate. We will compare the proposed methods to the current standards for summarising treatment effects on multiple outcomes.We propose to apply the methods to different clinical settings, develop methods to specify the relationships between outcomes and collaborate with clinical colleagues and decision makers to ensure that the assumptions are sensible and not contradicted by available data. We aim to develop a strategy which can be followed in order to obtain clinical opinion on plausible relationships between the outcomes, implement them in a statistical model and evaluate that model using available statistical software, to check that the assumptions made are appropriate.