Title: Synthesis of continuous outcomes including time to event: Their evaluation and subsequent adaption to the demands of clinical decision making
Funder: Department of Health and Social Care

Abstract: Background: Evidence synthesis is a well-established component of HTA applied to quantitatively combine the data from multiple trials in order to obtain an overall pooled estimate of clinical effectiveness which may be used to inform an associated economic evaluation and form the basis of healthcare decision making by organisations such as the National Institute for Health and Care Excellence (NICE). While there is a wealth of literature on evidence synthesis methods for combining dichotomous outcomes, there is much less on the synthesis of continuous outcomes despite them often being more complex to deal with due to (i) heterogeneous reporting across trials, (ii) higher propensity for outcomes to be censored/missing, and (iii) difficultly in interpreting results for decision making (e.g. standardised scales).Aims: The overall aim of this fellowship is to improve the synthesis methods used for continuous outcomes, including time-to-event, for HTA and thus improve the reliability of downstream decision making. This fellowship will address four specific issues: (i) uptake, and barriers to uptake, of methods proposed in the literature for synthesising continuous outcomes within a HTA setting; (ii) evaluation of methods in terms of ability to produce reliable and robust estimates of intervention effects and their associated uncertainty; (iii) ease of implementation of methods by analysts with an array of statistical expertise (i.e. from systematic reviewers to evidence synthesis statistical specialists); and (iv) clear communication of results to aid interpretability by all stakeholders including decision makers and clinicians.Plan of investigation: To achieve the above aims the fellowship will consist of four work packages: WP1: Review of current methods for synthesising continuous outcomes, including time-to-event, and explore how they have been applied in practice; WP2: Evaluation of methods for synthesising time-to-event data to inform estimates of comparative effectiveness through practical applications and a simulation study, to evaluate the robustness of the different methods when the assumptions made by the model are not met; WP3: Evaluation of methods for synthesising continuous outcomes measured on different scales across studies through practical application and a targeted simulation study; WP4: Development of guidance documents for analysts and a workshop for key stakeholders to disseminate findings.Summary of benefits: This fellowship will involve evaluation and, where necessary, development of methods together with recommendations that will allow evidence synthesis methods for continuous outcomes to be applied more confidently; maximising the evidence used whilst addressing heterogeneity. This will benefit stakeholders as it will lead to more reliable and robust estimates of intervention effects and their associated uncertainty, and thus more informed decision making. Evidence-based decision making can affect the types of interventions that are available on the NHS which in turn can change the care that is delivered by doctors and nurses often resulting in cost savings for the NHS and better quality of treatment for patients. In addition, the development of companion software and data visualisation approaches will facilitate uptake and interpretability by a broad array of stakeholders, especially important as methodology becomes increasingly more complex.

Lay abstract: Background: In order to decide what treatments are best for a given health condition and whether they are good value for money, we use an established set of techniques called 'health technology assessment'. Health technology assessment can help to change healthcare policy. For example, it can help to decide what sorts of interventions work best in the NHS and change the way that doctors and nurses provide care. As a result, there are likely to be cost savings for the NHS and better treatments for patients. Improved care improves the health of the public, which also benefits society and the economy.A major part of health technology assessment involves reviewing and combining findings from clinical trials to see how well a particular treatment works. We can also compare different treatments to see if one works better than another. This is quite easy if we have a clear-cut definition of whether a treatment 'works' or not. However, sometimes we have to use a less clear-cut measure for deciding whether the treatment is useful. For example, we might look at how much a person's pain has reduced, how much their depression or anxiety has improved, or how much longer they live after they have been diagnosed with a serious illness. These measures are called 'continuous outcomes'. They are often harder to work with, because different clinical trials can use them differently so it can be difficult to compare them.Design and methods: For this project, I would like to study some new methods for comparing trials that use continuous outcomes. I will compare these new methods by using both real and simulated (i.e. made up) data. I will take the best bits from different approaches and adapt them. I will also develop clear visual ways of presenting the results to help statisticians (i.e. people who use these methods) to tell doctors and other relevant healthcare professionals about them.Dissemination: Once I have finished this work, I will hold a workshop to present the findings to doctors, nurses, decision makers and statisticians. I hope that there will be lots of discussion about the approaches that will allow me to recommend research priorities in this area.Finally, I will produce guidelines about the recommended approaches for statisticians to use in the future. I want the findings of my research to have a quick and meaningful impact on healthcare.

Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE