HCD: Synthesis of networks of evidence on test accuracy, with and without a 'gold standard'

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A diagnostic test is any kind of medical test or assessment used to determine whether an individual does or does not have a disease or clinical condition. For most diseases there are multiple possible tests that could be used, each with different characteristics (e.g. accuracy, invasiveness to the patient, ease and speed of use, cost). Healthcare providers, laboratories and policy makers are faced with decisions about which test - or combination of tests - to use in practice for each disease.Although there are many factors to consider in making these decisions, one key consideration is the accuracy of each test. Most diagnostic tests do not have perfect accuracy: there is almost always a chance of some false positive and/or false negative results. Clearly, other factors being equal, tests that make fewer such errors are preferred. Information on the accuracy of any given test is very often available from multiple studies, and this information is statistically combined. These 'pooled' estimates are used for decision making. For example, they are a key component of 'decision models', used by bodies such as the National Institute for Health and Care Excellence (NICE) in the UK to estimate and compare the effectiveness and cost-effectiveness of different testing strategies. Methods for combining information from multiple studies on the accuracy of a single test are now well established. But these are inadequate for answering clinically important questions about how the accuracy of two or more tests compares and about the accuracy of tests used in combination. One of the difficulties is that different studies tend to report data of very different types: for example, Study 1 reports data on the accuracy of Tests A and B and also reports the overlap between test results on A and B; Study 2 reports data on the accuracy of A and B but doesn't report the amount of overlap; Study 3 reports on the accuracy of test A only; while Study 4 reports on tests B and C etc. A general modelling framework is needed that can analyse all such data, i.e. 'networks of evidence', together. An additional problem is that standard methods are based on a key assumption that accuracy can be (and has been in all studies, e.g. 1-4 in the example above) estimated directly by comparing test results with results from a 'gold standard' test. This is a test that is assumed to be error-free, i.e. perfectly accurate, but not fit to be used routinely on all patients (for example, it may be highly invasive or very expensive). In practice, often either no such test for a given disease exists, or it has not been applied in all studies. As a result of this unrealistic assumption, many estimates of test accuracy - and subsequent estimates that are reliant on these, e.g. of effectiveness and cost-effectiveness - could be completely wrong. However, careful modelling of networks of evidence will offer a route to relaxing this assumption, through a type of more advanced statistical modelling called 'latent class models'. Through modelling of the overlap between results on multiple tests applied to the same individuals, latent class models are able to provide the required estimates of test accuracy without any direct classification of each individual as diseased/disease-free. In this program of research we will develop a general statistical modelling framework to model networks of evidence on test accuracy, that will be applicable across wide ranging clinical areas. The approach will deliver more reliable estimates of the accuracy and comparative accuracy of tests or combinations of tests - ultimately leading to improved decisions about use of tests in practice. We will provide training and resources to support use of the methods developed.