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。可能存在一种高精确度、但也具有高度侵入性和/或昂贵的疾病检测方法(“黄金标准”或参考测试)。侵入性较小和/或成本较高的测试(“指数测试”)的准确性显然是人们非常感兴趣的。假设参考测试正确地对所有患者进行了分类，则指数测试的准确性可以用两个度量来量化：(1)灵敏度，定义为指数测试中正确测试为阳性的患病个体的比例；(2)特异度，即正确测试为阴性的非疾病个体的比例。在实践中，大多数指数测试并不能直接得出“有病”或“无病”的结果。通常，检测会以连续的尺度传递一个数字，例如血液中某种物质的浓度。如果患者的测试结果大于某一临界值，则被归类为患病患者。当此值在其可能范围内减小时，敏感度增加，但以降低特异度为代价。这种关系的曲线图称为接收器工作特性(ROC)曲线。即使在没有明确的连续测量的情况下，这通常也可以得出结论，因为，例如，一些临床医生在解读图像时往往会比其他医生对“疾病”的定义更严格。通常，对一项检测的敏感性和特异性的评估可以从多项研究中获得。与医学的其他领域(例如，治疗效果)一样，最好汇集这些估计，总结所有可用的数据。这被称为‘荟萃分析’。诊断测试准确性的Meta分析通常被认为比其他领域的分析更复杂，因为测试准确性有两个维度。此外，尽管患者群体和研究设计的可变性在荟萃分析的所有领域都是一个令人担忧的问题，但对于诊断测试的准确性，也有一个特别的担忧，即研究可能使用了不同的截止值。因此，这些荟萃分析往往表现出高度的研究间变异性。这导致了关于测试准确性的非决定性的总结证据，以及对最佳测试策略做出决定或建议的困难。诊断测试准确性的荟萃分析的标准方法仅汇集了来自每项研究的一对敏感性和特异性。然而，经常有研究报告ROC曲线上有多个点。此外，研究经常报告使用的截止值(S)，但这些通常不会被纳入模型。直观地说，这两种通常被丢弃的信息提供了巨大的潜力来解释研究之间的一些变异性，帮助我们更好地理解证据，特别是选择最佳的分界点。荟萃分析方法侧重于产生敏感性和特异性的汇集估计，或“总结”ROC曲线。但是，在实践中，诸如是否检测、使用哪种检测以及截止日期等重要决定也应基于其他信息：例如，根据检测结果提供的治疗效果、人群中的疾病数量和相关成本。测试策略的成本效益可以通过决策模型来量化，但对于如何从元分析结果中最好地告知该模型，仍存在问题。此外，到目前为止，关于选择替代测试的方法的工作相对较少。在这个项目中，我将评估在这个研究领域中已经提出的一些复杂的方法。此外，我将致力于以下方面的进一步方法开发：(I)充分利用关于测试的“所有可用证据”；(Ii)如何基于测试准确性的元分析最好地为决策模型提供信息；(Iii)模拟测试比较和在多个诊断测试之间进行选择。

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.