ABSTRACT

The evaluation of diagnostic tests usually involves statistical inference for its sensitivity. As sensitivity is defined as the probability that the test result will be positive when the target condition is present, the key study design consideration of sample size is the determination of the number of subjects with the target condition such that the estimation has adequate precision, or the hypothesis testing has adequate power. Traditionally, one may rely on prospective screening of subjects to obtain the required sample size, which means that if the prevalence of the disease is very low, a large number of subjects would need to be screened, increasing the study duration and cost. In this paper, we consider the possibility of substantially reducing the length and cost of a clinical study by leveraging subjects from a real-world data (RWD) source, focusing specifically on the diagnostic test for the cancer of interest. Using the propensity score methodology, we developed a procedure which ensures that the real-world subjects being leveraged are similar to their prospectively enrolled counterparts, thereby making the leveraging more justified. The procedure allows the down-weighting of the real-world subjects, which can be achieved by either using a Frequentist’s method based on the composite likelihood or a Bayesian method based on the power prior. The proposed approach can be applied to the evaluation of any diagnostic test and it is not limited to the current clinical study regarding a cancer diagnostic test. Notably, this paper is in close alignment with a recently released draft framework by the Medical Device Innovation Consortium (MDIC) on real-world clinical evidence and in vitro diagnostics, being a showcase of appropriately leveraging real-world data in diagnostic test evaluation for diseases with low prevalence to support regulatory decision-making.

摘要

诊断测试的评估通常涉及对其敏感性的统计推断。由于敏感性被定义为存在目标条件时测试结果为阳性的概率，样本量的关键研究设计考虑是确定具有目标条件的受试者数量，以便估计具有足够的精确度，或假设检验有足够的功效。传统上，人们可能依赖于对受试者的前瞻性筛查来获得所需的样本量，这意味着如果疾病的患病率非常低，则需要对大量受试者进行筛查，从而增加研究持续时间和成本。在本文中，我们考虑了通过利用来自真实世界数据 (RWD) 源的受试者来大幅减少临床研究的长度和成本的可能性，专注于感兴趣的癌症的诊断测试。使用倾向评分方法，我们开发了一个程序，确保被利用的现实世界受试者与其预期注册的对象相似，从而使利用更加合理。该过程允许对现实世界的主题进行加权，这可以通过使用基于复合似然的频率论方法或基于功率先验的贝叶斯方法来实现。所提出的方法可以应用于任何诊断测试的评估，并且不限于当前关于癌症诊断测试的临床研究。值得注意的是，本文与医疗器械创新联盟 (MDIC) 最近发布的关于真实世界临床证据和体外诊断，展示了在对低流行疾病的诊断测试评估中适当利用真实世界数据以支持监管决策。