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

诊断测试是用于确定个体是否患有疾病或临床状况的任何类型的医学测试或评估。对于大多数疾病，可以使用多种可能的测试，每种测试都具有不同的特征（例如准确性，对患者的侵入性，易用性和使用速度，成本）。医疗服务提供者、实验室和政策制定者都面临着在实践中对每种疾病使用哪种检测或检测组合的决定。尽管在做出这些决定时需要考虑许多因素，但其中一个关键因素是每种检测的准确性。大多数诊断测试不具有完美的准确性：几乎总是有一些假阳性和/或假阴性结果的机会。显然，在其他因素相同的情况下，产生较少此类错误的测试是优选的。关于任何给定测试的准确性的信息通常可以从多个研究中获得，并且这些信息在统计上是组合的。这些“汇总”估计用于决策。例如，它们是“决策模型”的关键组成部分，由英国国家健康与护理卓越研究所（NICE）等机构用于评估和比较不同测试策略的有效性和成本效益。现在，将多项研究的信息结合在一起以确定单一测试的准确性的方法已经建立起来。但这些不足以回答临床上重要的问题，即两种或多种测试的准确性如何比较，以及组合使用的测试的准确性。困难之一是，不同的研究往往报告非常不同类型的数据：例如，研究1报告了关于测试A和B的准确性的数据，也报告了关于A和B的测试结果之间的重叠;研究2报告了关于A和B的准确性的数据，但没有报告重叠的量;研究3只报告了测试A的准确性;研究4报告了测试B和C等。需要一个通用的建模框架，可以一起分析所有这些数据，即“证据网络”。另一个问题是，标准方法是基于一个关键假设，即准确性可以（并且已经在所有研究中，例如上述示例中的1-4）通过将测试结果与“金标准”测试的结果进行比较来直接估计。这是一种假设为无误差的测试，即完全准确，但不适合常规用于所有患者（例如，它可能具有高度侵入性或非常昂贵）。在实践中，通常不存在针对特定疾病的这种测试，或者它尚未应用于所有研究。由于这种不切实际的假设，许多测试准确性的估计-以及随后依赖于这些估计的估计，例如有效性和成本效益-可能是完全错误的。然而，仔细的证据网络建模将通过一种称为“潜在类别模型”的更先进的统计建模提供一种放松这一假设的途径。通过对应用于相同个体的多个测试结果之间的重叠进行建模，潜在类别模型能够提供所需的测试准确性估计，而无需将每个个体直接分类为患病/无病。在这项研究计划中，我们将开发一个通用的统计建模框架，以模拟测试准确性的证据网络，这将适用于广泛的临床领域。该方法将提供更可靠的准确性和测试或测试组合的相对准确性的估计-最终导致在实践中使用测试的改进决策。我们将提供培训和资源，以支持使用所开发的方法。

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.