Evidence synthesis of diagnostic test performance from a decision-making perspective

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A diagnostic test is any kind of medical test or assessment used to determine whether a patient does or does not have a disease. A highly accurate, but also highly invasive and/or expensive, test for a disease might exist (the 'gold standard' or reference test). The accuracy of less invasive and/or expensive tests ('index tests') is obviously of great interest. Assuming that the reference test has correctly classified all patients, the accuracy of the index test can be quantified using two measures: (1) the sensitivity, defined as the proportion of diseased individuals who correctly test positive on the index test, and (2) the specificity, the proportion of non-diseased individuals who correctly test negative. In practice, most index tests do not directly deliver a 'disease' versus 'no disease' outcome. Often the test delivers a number on a continuous scale, for example the concentration of some substance in the blood. A patient is classified as diseased if his/her test result is greater than some cut-off value. As this value is reduced over its possible range, sensitivity is increased, but at the cost of reduced specificity. A graph of this relationship is called the Receiver Operating Characteristic (ROC) curve. This can often be drawn even for tests without an explicit continuous measure since, for example, some clinicians interpreting images will tend to have a stricter definition of 'disease' than others. Often estimates of the sensitivity and specificity of a test are available from multiple studies. As in other areas of medicine (e.g. treatment effectiveness), it is desirable to pool these estimates, to summarise all available data. This is called 'meta-analysis'. Meta-analysis of diagnostic test accuracy is often considered more complex than that in other areas, due to there being two dimensions of test accuracy. Further, although variability in e.g. patient populations and study designs is a concern throughout all areas of meta-analysis, for diagnostic test accuracy there is also the specific concern that studies are likely to have used different cut-off values. These meta-analyses therefore tend to exhibit a high degree of between-study variability. This leads to inconclusive summary evidence on test accuracy, and difficulty in making decisions or recommendations about the best testing strategies.Standard methods for meta-analysis of diagnostic test accuracy pool only a single pair of sensitivity and specificity from each study. However, often studies report multiple points on the ROC curve. In addition, studies often report the cut-off value(s) used, but these are not usually incorporated into the model. Intuitively, these two types of information, both usually discarded, offer great potential to explain some of the between-study variability, helping us to make better sense of the evidence and, in particular, to choose the best cut-off.Meta-analysis methods focus on producing pooled estimates of sensitivity and specificity, or a 'summary' ROC curve. But, in practice, important decisions such as whether to test, which test to use and at which cut-off should also be based on other information: e.g. the effectiveness of treatments to be provided in light of test results, the amount of disease in the population, and relevant costs. The cost-effectiveness of testing strategies can be quantified by a decision model, but there are questions regarding how best to inform this model from the meta-analysis results. In addition, there has been relatively little work to date on methods for choosing between alternative tests. In this project, I will evaluate some sophisticated methods that have been suggested in this research area. In addition, I will work on further methods development for: (i) making full use of 'all available evidence' on testing (ii) how best to inform a decision model based on a meta-analysis of test accuracy, (iii) modelling test comparisons and choosing between multiple diagnostic tests.