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

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.

In health technology assessment (HTA) reimbursement decisions are made based on economic evaluation of new health technologies by taking into account clinical effectiveness and health related quality of life (HR QoL) estimates. Clinical effectiveness is estimated by meta-analysing clinical outcomes obtained from randomised controlled trials (RCTs). To measure these outcomes, however, often requires extended follow up time of the RCTs. Therefore surrogate outcomes, especially those that can be measured early, are increasingly investigated as endpoints in RCTs and HTA where they are used to predict longer term clinical endpoints of the primary interest or HR QoL.

Multivariate meta-analysis (MVMA) methods are most suitable to evaluate surrogate endpoints as they take into account the correlation between the outcomes and all associated uncertainty. Bayesian methods have the advantage of allowing analysts to make direct probability statements about predictions. Also in this framework external information (based on external data or expert opinions) can be incorporated in a model in the form of prior distributions facilitating synthesis of all available evidence and potentially leading to the more accurate probability statements and hence more efficient decision making process.

Novel Bayesian MVMA methods will be developed for evaluating surrogate endpoints, taking into account appropriate uncertainty and inter-relationships between all model parameters. The work will be extended to multiple surrogate outcomes (to include all relevant studies) and multivariate indirect comparisons and network meta-analysis (NMA) methods. Extending evidence base to a wider range of interventions in NMA may lead to a wider range of reported outcomes, therefore the development of multivariate IC and NMA methods will be required. Methods for mapping surrogate outcomes onto utility measures, such as EQ-5D commonly used in economic evaluation, will also be developed.

Potential beneficiaries include stakeholders in all sectors of health care:

1. Policy decision makers in reimbursement agencies such as the National Institute for Health and Clinical Excellence (NICE) in England and Wales, the Canadian Agency for Drugs and Technologies in Health (CADTH) or the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia
2. Regulatory agencies such as the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) in the US
3. Pharmaceutical companies and commercial clinical research organisations (CROs)
4. Charities who conduct research in all clinical disciplines
5. Patients

The proposed research has the potential to impact on the nation's health by increasing the effectiveness of policy decision making in health care. Methodology developed in this research programme will provide guidance to analysts on the appropriate inclusion of surrogate endpoints in health technology assessment and therefore will lead to a faster and more efficient decision making process by the reimbursement agencies such as NICE in England and Wales (and similar agencies in Europe, Canada and Australia). The methods will enhance analysts' abilities of identifying the best treatment options with fewer adverse effects leading to impact on health and health related quality of life. The research will also contribute to the further development of meta-analytical methods for validating surrogate endpoints and hence facilitate the design of clinical trials of shorter duration which will speed up the drug development process and the regulatory processes of agencies such as the EMA and the FDA. Both the FDA and EMA are already seeking to make use of recent methodological tools, that include surrogate endpoints and biomarkers, through their initiatives such as the EMA's Think-Tank group on Innovative Drug Development and the FDA's Critical Path Initiative which aim to increase the efficiency of drug development. A faster drug development process will lead to a healthier society (patients) and better economy (pharmaceutical industry).

The methods developed will be published within months and can be implemented immediately in HTA. Staff employed on the grant will develop statistical, analytical and computing skills transferable to all employment sectors including academia, clinical research organisations, industry and government. The research may also contribute to the development of the curriculum of the masters course in Medical Statistics, run by the Department of Health Sciences, University of Leicester, graduates of which are trained for employment in all the relevant sectors.