Cancer Recurrence: Detection in Administrative Data, Incidence, and Costs

描述（由申请人提供）：虽然一些癌症死亡可归因于原发疾病的进展，但许多（如果不是大多数）是由于早期疾病成功的最终治疗后发生的复发性转移性癌症。大多数肿瘤登记，包括SEER，没有记录复发。因此，我们对基于人群的晚期复发性癌症的发病率、模式和治疗结果知之甚少。一个有效和可靠的算法来识别管理数据中的这种重复，将使对这一常见、昂贵和致命的疾病的比较有效性研究得到真正的爆炸式增长。特别是，一种可用于识别管理数据中复发的算法将使以下两点成为可能：(1)开展以无病生存期为结果的研究；(2)能够确定初始队列，以研究晚期复发性疾病的治疗模式和结果。通过丹娜-法伯癌症研究所和癌症研究网络研究者之间现有的多学科合作，我们在复发算法的开发方面取得了相当大的进展，在两个独特的数据集中工作，这些数据集包含与复发金标准数据相关的完整声明。到目前为止，我们已经表明，发表的复发识别策略在我们最近的基于人群的数据集中具有不可接受的低灵敏度和特异性，并且已经开发了一个非常有前途的两阶段概率模型，首先确定复发的概率，然后估计它发生的日期。我们现在建议在这项工作的基础上，进一步开发和验证该算法，然后将其应用于生成复发性晚期癌症造成的公共卫生负担的政策相关数据。具体来说，我们将：(1)通过交叉验证估计和严格评估算法性能，完成一种候选算法的开发，用于检测非转移性肺癌、结直肠癌、乳腺癌和前列腺癌的最终治疗后复发；(2)采用新方法在几个全新的数据集中直接或间接地验证算法；(3)应用

DESCRIPTION (provided by applicant): While some cancer deaths are attributable to progression of the primary disease, many, if not the majority, are due to recurrent metastatic cancer that develops after successful definitive therapy for earlier stage disease. Most tumor registries, including SEER, do not capture recurrence. Therefore, remarkably little is known about the population-based incidence or patterns and outcomes of care for advanced recurrent cancer. A valid and reliable algorithm for identifying such recurrences in administrative data would enable a literal explosion of comparative effectiveness research on this common, costly, and lethal condition. In particular, an algorithm that could be used to identify recurrence in administrative data would make it possible to (1) conduct studies using disease-free survival as an outcome, and (2) would enable the identification of inception cohorts in whom to study patterns and outcomes of care for advanced recurrent disease. Through an existing multidisciplinary collaboration between Dana-Farber Cancer Institute and Cancer Research Network investigators, we have made considerable progress on the development of a recurrence algorithm, working in two unique data sets that contain complete claims linked to gold standard data on recurrence. To date, we have shown that published recurrence identification strategies have unacceptably low sensitivity and specificity in our recent, population-based data sets, and have developed a highly promising two-phase probabilistic model that first determines the probability of recurrence and then estimates the date on which it occurred. We now propose to build on this work, conducting further development and validation of the algorithm, and then applying it to generate policy-relevant data on the public health burden imposed by recurrent advanced cancer. Specifically, we will: (1) complete the development of a candidate algorithm for detecting recurrence after definitive therapy of non-metastatic lung, colorectal, breast, and prostate cancer by incorporating use of cross-validation estimates and rigorously assessing algorithm performance~ (2) employ novel methods to directly and indirectly validate the algorithm in several entirely new data sets~ and (3) apply the validated algorithm to estimate the proportion of all-cause mortality attributable to recurrence and the total annualized costs of care for patients with recurrent disease, compared to patients presenting with advanced disease at diagnosis. As more cancer patients survive and survive longer, the population at risk for recurrence increases. Our algorithm will enable a new generation of research on the effectiveness, quality, and outcomes of cancer care that takes into account this sentinel event in the cancer trajectory. In our applied studies, we will begin to capitalize on this opportunity by measuring the impact of this condition on the public health and the consumption of societal resources.