Abstract：
Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.
摘要：
虚拟临床试验（VCT）作为定量预测人群中异质治疗反应的工具越来越受欢迎。在VCT的背景下，例如,似是而非的患者是具有参数(或属性)值的数学模型的实例,所述参数(或属性)值被选择以反映针对该特定患者的疾病的特征和对治疗的响应。已经引入了许多技术来确定要包括在虚拟患者队列中的模型参数化的集合。这些方法通常从每个模型参数的先验分布开始，并利用一些标准来确定从先验采样的参数集是否应该包括或排除在合理的群体中。没有标准技术存在，然而,用于生成这些先验分布并选择纳入/排除标准。在这项工作中,我们严格量化VCT设计选择对VCT预测的影响.而不是使用真实的数据和复杂的数学模型，放射治疗的空间模型用于生成模拟患者数据，用于描述患者数据的数学模型是双参数常微分方程模型。这种受控的设置使我们能够分离出先验分布和纳入/排除标准对似然人群的异质性和预测的治疗反应的影响。我们发现先验分布，而不是纳入/排除标准，对似然种群的异质性有较大的影响。然而，合理人群中治疗应答者的百分比对所采用的纳入/排除标准更敏感.对虚拟临床试验设计的作用的这种基本理解应该有助于为使用更复杂的模型和真实数据的未来VCT的开发提供信息。
