HCD: Novel approaches of multi-parameter evidence synthesis and decision modelling for efficient evaluation of diagnostic health technologies

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Effective and accurate assessment of diagnostic tests is crucial from the point of view of patient care, development of new therapies and allocation of resources in health services such as NHS. Decision makers, such as the National Institute for Health and Care Excellence (NICE) in the UK make recommendations regarding the uptake of the new diagnostic tools. Such assessments are complex as they are based not only on an assessment of the accuracy of the diagnostic tests, but also on clinical outcomes in patients diagnosed using these tests and cost effectiveness of both the diagnostic tests and the treatments patients receive following the diagnosis.Following recent advances in science, a multitude of novel diagnostic tools, as well as pharmacological therapies closely linked to these diagnostic technologies, have been developed. Some of the tests are genetic biomarkers, others are imaging techniques (such as magnetic resonance imaging). Many novel diagnostic tools can speed up patients' diagnosis, thus improving their chances for successful treatment. Other diagnostic tests (or biomarkers) are used to identify groups of patients who can benefit from novel therapies. These advances, however, bring an additional layer of complexity when it comes to evaluation of new diagnostic health technologies, due to complexity of available evidence. In this project, we aim to develop statistical tools to help in synthesis of such complex data. For example, often multiple test combinations need to be used when patients are being diagnosed or their prognosis is assessed. We will develop methods which will take into account interdependencies between these tests to ensure that decisions about the uptake of such new diagnostic tools are based on all relevant evidence. Such evidence also includes outcomes of patients. When patients are diagnosed as positive for certain biomarkers, their outcomes are often evaluated in clinical trials and data from these trials need to be combined to make efficient assessments. Such clinical trials, however, differ in the way they are designed generating heterogeneous data, varying with respect to the types of patients being included, and are therefore difficult to combine. To overcome this challenge, we will develop methods for efficient synthesis of evidence on the effectiveness of prognostic biomarkers and related therapies from heterogeneous study designs. Evidence from clinical trials or diagnostic test accuracy studies may be not only heterogeneous but also limited. We will investigate how the use of electronic health records, such as data from cohort studies or patient registries, can help to generate more robust evidence for assessment of diagnostic tools.In the final part of our project we will investigate how the methods developed in earlier parts of the project can be used to effectively inform models evaluating cost-effectiveness of new diagnostic technologies, including prognostic biomarkers. The accuracy and cost-effectiveness of diagnostic tests cannot be evaluated in isolation, as a combination of data on their ability to correctly diagnose patients, their ability to predict treatment effects, and their impact on clinical decisions about treatment options should be taken into account. All these components that impact on the decision about the uptake of new diagnostic tests result from different parts of analysis of different sources of evidence and the estimates of these analyses come with certain level of uncertainty, because they are based on relatively small subsets of the population. Including information on the accuracy of potentially multiple diagnostic tests (and related treatments) in a decision modelling framework, whilst taking into account their dependencies and related uncertainty, is a very complex undertaking. We will explore optimal methods for combining all this information in a decision framework.