Bayesian multivariate evidence synthesis methods to incorporate surrogate endpoints in health care evaluation

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In health technology assessment (HTA) decisions are made, by agencies such as NICE, about the availability of new health technologies in health care systems such as the NHS in the UK. There has been a recent trend towards clinical trials of shorter duration and towards faster decision making. When evaluating new health technologies, such as new medications, devices or surgical procedures, effectiveness outcomes are measured in randomised controlled trials (RCTs). However, measuring a final clinical outcome may require extended follow-up time. Hence shorter term endpoints are increasingly sought as surrogates for long term endpoints to expedite the drug development process. Candidate surrogate endpoints need to be validated to ensure that the treatment effect measured by those endpoints predicts well the treatment effect that would be measured by the true outcome. For example, in cancer the effect of treatment measured by comparing overall survival (OS) of patients in the new treatment group with survival of those in the control group can be predicted by measuring progression free survival (PFS), the survival without relapse or progression. PFS is measured early compared to OS, hence PFS is a desirable surrogate endpoint in clinical trials. Surrogate outcomes can be used to predict clinical effectiveness or be mapped onto health-related quality of life measures, both used in economic evaluation. Hence the use of surrogate endpoints can lead to faster decision-making process.The proposed project aims to assess current methodology and develop further suitable methods to evaluate surrogate endpoints and incorporate them in HTA. A number of meta-analytical methods have been proposed that aim to evaluate surrogate outcomes. Different approaches are based on different modelling assumptions and take into account different levels of uncertainty about parameters of such models. This may impact on accuracy of the validation and predictions. Multivariate meta-analysis methods are most suitable to evaluate surrogate outcomes as they take into account the relationship between outcomes. Bayesian methods have the advantage of allowing analysts to make direct probability statements about predictions, for example about the probability that the new treatment is superior to the standard care when measured by the true outcome given that it is superior when measured by the surrogate outcome. Bayesian methods are also unique in their ability to include external information, based on external clinical data or expert opinions in a model in the form of so called prior distributions. This approach allows us to take into account all available evidence in HTA with the goal of making the probability statements more accurate, leading to more efficient decision making.Novel evidence synthesis methods will be developed to incorporate surrogate endpoints in HTA. The project will extend the applicant's recent work on multivariate meta-analysis by investigating alternative modelling assumptions (about the relationship between parameters of the model and between the surrogate and clinical outcomes) to ensure they are suitable in different health care settings. The work will be extended to multiple surrogate outcomes and indirect comparisons (IC) and network meta-analysis (NMA) methods. When data from trials comparing two treatments is limited, IC and MTC allow us to estimate the relative effect between those treatments by use of indirect evidence from studies using different treatment comparators. Studies evaluating a wider range of interventions may report a wider range of outcomes. Hence there is a need to develop multivariate IC and NMA methods. Methods will also be developed to incorporate surrogate outcomes in economic evaluation of heath technologies, by using multivariate methods to map disease specific effectiveness and health related quality of life measures onto standard quality of life endpoints (usually EQ-5D) used in economic evaluation.