HCD: Novel approaches of multi-parameter evidence synthesis and decision modelling for efficient evaluation of diagnostic health technologies

从病人护理、开发新疗法和国家医疗服务体系等卫生服务资源分配的角度来看，对诊断测试进行有效和准确的评估至关重要。决策者，如英国国家健康与护理卓越研究所（NICE），就采用新的诊断工具提出建议。这种评估是复杂的，因为它们不仅基于对诊断测试准确性的评估，而且基于使用这些测试诊断的患者的临床结果以及诊断测试和患者在诊断后接受的治疗的成本效益。随着科学的最新进展，许多新的诊断工具以及与这些诊断技术密切相关的药理学疗法已被开发出来。一些测试是基因生物标记，其他是成像技术（如磁共振成像）。许多新的诊断工具可以加快患者的诊断，从而提高他们成功治疗的机会。其他诊断测试（或生物标记物）用于确定可以从新疗法中受益的患者群体。然而，由于现有证据的复杂性，这些进步在评估新的诊断卫生技术时又增加了一层复杂性。在这个项目中，我们的目标是开发统计工具来帮助合成这些复杂的数据。例如，在对患者进行诊断或评估其预后时，通常需要使用多种检测组合。我们将制定考虑到这些检测之间相互依赖关系的方法，以确保采用这些新诊断工具的决定是基于所有相关证据。这些证据还包括患者的结果。当患者被诊断为某些生物标志物阳性时，他们的结果通常在临床试验中进行评估，这些试验的数据需要结合起来进行有效的评估。然而，这些临床试验的不同之处在于它们的设计方式产生了不同的数据，这些数据因纳入的患者类型而异，因此难以合并。为了克服这一挑战，我们将开发方法来有效地合成来自异质研究设计的预后生物标志物和相关疗法有效性的证据。来自临床试验或诊断测试准确性研究的证据可能不仅是异质的，而且是有限的。我们将调查如何使用电子健康记录，例如来自队列研究或患者登记的数据，有助于为诊断工具的评估提供更有力的证据。在我们项目的最后一部分，我们将研究项目早期开发的方法如何有效地为评估新诊断技术的成本效益的模型提供信息，包括预后生物标志物。诊断测试的准确性和成本效益不能孤立地进行评估，因为应综合考虑其正确诊断患者的能力、预测治疗效果的能力以及对有关治疗方案的临床决策的影响的数据。所有这些影响是否采用新诊断测试的决定的因素都来自对不同证据来源的分析的不同部分，而且这些分析的估计具有一定程度的不确定性，因为它们是基于相对较小的人群子集。在决策建模框架中包括关于可能多种诊断测试（和相关治疗）准确性的信息，同时考虑到它们的依赖性和相关的不确定性，是一项非常复杂的工作。我们将探索在决策框架中结合所有这些信息的最佳方法。

Effective and accurate assessment of diagnostic tests is crucial from the point of view of patient care, development of new therapies and allocation of resources in health services such as NHS. Decision makers, such as the National Institute for Health and Care Excellence (NICE) in the UK make recommendations regarding the uptake of the new diagnostic tools. Such assessments are complex as they are based not only on an assessment of the accuracy of the diagnostic tests, but also on clinical outcomes in patients diagnosed using these tests and cost effectiveness of both the diagnostic tests and the treatments patients receive following the diagnosis.Following recent advances in science, a multitude of novel diagnostic tools, as well as pharmacological therapies closely linked to these diagnostic technologies, have been developed. Some of the tests are genetic biomarkers, others are imaging techniques (such as magnetic resonance imaging). Many novel diagnostic tools can speed up patients' diagnosis, thus improving their chances for successful treatment. Other diagnostic tests (or biomarkers) are used to identify groups of patients who can benefit from novel therapies. These advances, however, bring an additional layer of complexity when it comes to evaluation of new diagnostic health technologies, due to complexity of available evidence. In this project, we aim to develop statistical tools to help in synthesis of such complex data. For example, often multiple test combinations need to be used when patients are being diagnosed or their prognosis is assessed. We will develop methods which will take into account interdependencies between these tests to ensure that decisions about the uptake of such new diagnostic tools are based on all relevant evidence. Such evidence also includes outcomes of patients. When patients are diagnosed as positive for certain biomarkers, their outcomes are often evaluated in clinical trials and data from these trials need to be combined to make efficient assessments. Such clinical trials, however, differ in the way they are designed generating heterogeneous data, varying with respect to the types of patients being included, and are therefore difficult to combine. To overcome this challenge, we will develop methods for efficient synthesis of evidence on the effectiveness of prognostic biomarkers and related therapies from heterogeneous study designs. Evidence from clinical trials or diagnostic test accuracy studies may be not only heterogeneous but also limited. We will investigate how the use of electronic health records, such as data from cohort studies or patient registries, can help to generate more robust evidence for assessment of diagnostic tools.In the final part of our project we will investigate how the methods developed in earlier parts of the project can be used to effectively inform models evaluating cost-effectiveness of new diagnostic technologies, including prognostic biomarkers. The accuracy and cost-effectiveness of diagnostic tests cannot be evaluated in isolation, as a combination of data on their ability to correctly diagnose patients, their ability to predict treatment effects, and their impact on clinical decisions about treatment options should be taken into account. All these components that impact on the decision about the uptake of new diagnostic tests result from different parts of analysis of different sources of evidence and the estimates of these analyses come with certain level of uncertainty, because they are based on relatively small subsets of the population. Including information on the accuracy of potentially multiple diagnostic tests (and related treatments) in a decision modelling framework, whilst taking into account their dependencies and related uncertainty, is a very complex undertaking. We will explore optimal methods for combining all this information in a decision framework.