Modeling ovarian cancer screening for CEA

描述(申请人提供)：对卵巢癌筛查的兴趣与日俱增。被诊断为卵巢癌的女性的五年生存率总体上为50%，但仅限于卵巢癌的女性的五年生存率为95%。然而，只有25%的卵巢癌在这个早期阶段被诊断出来，这表明通过早期发现有机会显著改善。我们的目标是通过考虑疾病和筛查人群中的异质性来提高先前开发的卵巢癌筛查微模拟模型的准确性，以便评估纵向使用标记面板来检测正在发展的疾病的成本效益。我们将扩大模型的范围，以适应1)一组用于筛查的血清标志物，以及2)基于风险的筛查。我们还将纳入筛查和疾病对生活质量的影响，并在筛查和治疗成本方面更新模型。这些努力将使我们能够确定潜在最有效的卵巢癌筛查策略，并报告其成本效益。 这项研究的具体目的有两个：一是开发最先进的卵巢癌筛查微模拟模型，二是利用该模型探索卵巢癌筛查替代策略的成本效益。目标2有两个组成部分：使用一组血清标志物和成像确定卵巢癌筛查的潜在成本效益策略，并根据风险水平评估这些策略在不同人群中的成本效益。 两项卵巢癌筛查的随机对照试验(RCT)正在进行中，一项在美国，一项在英国，但结果还需要几年时间才能公布。无论随机对照试验的结果如何，关于成本效益、更频繁的筛查的有效性以及多种标志物和成像的创新使用的问题仍将存在。分子发现很可能很快就会产生一组标记，这些标记可以一起用作涉及成像的多模式策略的第一线屏幕。由于进行新的随机对照试验以测试每一种可能更好的筛查策略的成本将高得令人望而却步，因此需要一个准确的模拟模型来制定合理的医疗保健政策，并指导未来在基础和应用水平上的研究。 为了提高模型预测的准确性，我们将对其进行改进，以考虑疾病的异质性(组织学、分级)和筛查人群的异质性(风险水平)。此外，我们将改进模型的检测组件，以更好地表示成像，并使用华盛顿州摄政王蓝十字和联邦医疗保险的保险索赔数据更新模型中使用的成本估计。将进行广泛的验证，以评估模型的预测与试验获得的估计的一致性。 归根结底，一个包含了新的发展、未来的创新以及我们今天认识的创新的增强型微观模拟模型，将有助于指导政策和研究投资决策。

DESCRIPTION (provided by applicant): Interest in screening for ovarian cancer is growing. Five-year survival in women diagnosed with ovarian cancer is 50% overall, but in women with cancer confined to the ovaries, it is 95%. Only 25% of ovarian cancer is diagnosed in this early stage, however, suggesting that there is an opportunity for significant improvement through early detection. Our goal is to improve the accuracy of a previously developed microsimulation model of ovarian cancer screening by accounting for heterogeneity in the disease and in the population screened, in order to evaluate the cost-effectiveness of using a marker panel longitudinally to detect developing disease. We will expand the scope of the model to accommodate use of 1) a panel of serum markers for screening, and 2) risk-based screening. We will also incorporate QOL effects of both screening and disease, and update the model with respect to screening and treatment costs. These efforts will enable us to identify the potentially most efficient strategies for ovarian cancer screening and to report their cost-effectiveness. The specific aims of this study are twofold: one, to develop a state-of-the-art microsimulation model of ovarian cancer screening and two, to use the model to explore the cost-effectiveness of alternative strategies for ovarian cancer screening. There are two components to aim 2: to identify potentially cost-effective strategies for ovarian cancer screening using a panel of serum markers and imaging and to estimate the cost-effectiveness of the strategies in various populations defined by risk level. Two randomized controlled trials (RCT) of ovarian cancer screening are underway, one in the U.S. and one in the U.K., but results will not be available for several more years. Regardless of the outcomes of the RCTs, questions about cost-effectiveness, the efficacy of more frequent screening, and innovative use of multiple markers and imaging will remain. Molecular discoveries are likely soon to yield a panel of markers that can be used together as a first-line screen in a multimodal strategy involving imaging. Because it would be prohibitively expensive to conduct new RCT to test each potentially better screening strategy, an accurate simulation model will be necessary to develop sensible health care policy as well as to direct future research at both the basic and applied level. To improve the accuracy of the model's predictions, we will refine it to account for heterogeneity in the disease (histology, grade) and heterogeneity in the population screened (risk level). In addition, we will refine the detection component of the model to better represent imaging, and update the cost estimates used in the model, using insurance claims data from Regence Blue Cross of Washington State and Medicare. Extensive validation will be undertaken to assess the consistency of the model's predictions with estimates obtained from trials. Ultimately, an enhanced microsimulation model that incorporates new developments, innovations of the future as well as those we recognize today, will help guide policy and research investment decisions.