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

诊断测试是用于确定患者是否患有疾病的任何类型的医学测试或评估。可能存在一种高度准确，但也是高度侵入性和/或昂贵的疾病测试（“金标准”或参考测试）。侵入性较小和/或昂贵的测试（“索引测试”）的准确性显然是非常感兴趣的。假设参考测试已经正确地对所有患者进行分类，可以使用两种测量来量化指标测试的准确性：（1）灵敏度，定义为在指标测试中正确地测试为阳性的患病个体的比例，以及（2）特异性，正确地测试为阴性的非患病个体的比例。在实践中，大多数指标测试并不直接提供“疾病”与“无疾病”的结果。通常测试提供一个连续的规模上的数字，例如血液中某些物质的浓度。如果他/她的测试结果大于某个截止值，则将患者归类为患病。当该值在其可能范围内降低时，灵敏度增加，但以降低特异性为代价。这种关系的图被称为受试者工作特征（ROC）曲线。即使对于没有明确的连续测量的测试，也可以经常得出这一点，因为例如，一些解释图像的临床医生往往比其他人对“疾病”有更严格的定义。通常从多个研究中可以获得测试的灵敏度和特异性的估计。与其他医学领域（如治疗有效性）一样，最好将这些估计值汇总起来，以总结所有可用数据。这就是所谓的“元分析”。诊断测试准确性的元分析通常被认为比其他领域更复杂，因为测试准确性有两个维度。此外，尽管患者人群和研究设计的变异性是荟萃分析所有领域的一个问题，但对于诊断测试准确性，还存在研究可能使用不同截止值的具体问题。因此，这些荟萃分析往往表现出高度的研究间变异性。这导致了对测试准确性的不确定的总结证据，并且难以做出关于最佳测试策略的决定或建议。用于诊断测试准确性的荟萃分析的标准方法仅从每个研究中汇集单个对的灵敏度和特异性。然而，通常研究报告ROC曲线上有多个点。此外，研究经常报告使用的临界值，但这些通常不纳入模型。直觉上，这两种类型的信息，通常都被丢弃，提供了很大的潜力来解释一些研究之间的变异性，帮助我们更好地理解证据，特别是选择最佳cut-cut-cut-off. Meta分析方法侧重于产生合并的敏感性和特异性估计值，或“摘要”ROC曲线。但是，在实践中，是否进行检测、使用哪种检测以及达到哪种临界值等重要决定还应基于其他信息：例如，根据检测结果提供的治疗的有效性、人群中的疾病数量以及相关费用。测试策略的成本效益可以通过一个决策模型来量化，但是关于如何最好地从荟萃分析结果中为这个模型提供信息，还有一些问题。此外，到目前为止，在选择替代测试的方法方面的工作相对较少。在这个项目中，我将评估一些先进的方法，已建议在这一研究领域。此外，我将致力于进一步的方法开发：（一）充分利用“所有可用的证据”的测试（二）如何最好地告知决策模型的基础上的荟萃分析测试的准确性，（三）模拟测试比较和选择之间的多个诊断测试。

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.