Refined Capture-Recapture Methods for Surveilling Cancer Recurrence

项目总结/摘要 监测疾病流行率和估计受影响的人数， 人口是慢性病和传染病流行病学监测的关键目标。这 该提案旨在提供新颖可靠的统计工具，以改进设计和分析的最佳做法， 这样的监测研究。我们从与注册相关的及时挑战中获得具体的动力- 基于格鲁吉亚州癌症登记处（GCR）的癌症复发监测。 我们专注于定制捕获-再捕获（C-R）方法，这些方法越来越多地用于 根据多个流行病学监测流估计病例或死亡总数。我们澄清 与广泛流行的基于对数线性模型的C-R技术相关的未被充分认识的陷阱，并提出 通过数据可视化进行敏感性分析的可访问方法， 将不确定性适当地传播到病例总数的最终估计中。这反过来又提供了一个网关， 一个广泛的有用的模型，从业者可以透明地编码假设如何 监测流在人口水平上相互作用。作为下一步，我们考虑 一种情况下，其中一个监测流是通过一个良好的控制抽样设计。下 在适当的条件下，这提供了我们所说的“锚流”， 克服了在指定可辩护的C-R模型时固有的不确定性。在这种情况下，我们将尽最大努力 通过一种新的C-R估计器估计病例总数的统计实践， 在锚流后面的原则性采样，同时提供显著提高的精度。我们建议 扩展这种方法来解释错误分类，这在我们的动机研究中是不可避免的。 癌症复发和在任何情况下，其中监测流确定的情况下，在一个容易出错的方式。 我们将根据乳腺癌和结直肠癌复发监测的建议方法， 正在进行的癌症复发信息和监测计划（CRISP），基于GCR。脆片是 从多达6个数据流中主动编译信息丰富但可能假阳性的复发信号，以及 通过基于方案的病历审查进行验证抽样，以确认 表示递归。我们将使用这些验证数据来调整基于C-R的估计中的错误分类。 复发计数。特别是目前项目将实行原则性的“锚流”随机抽样 200名GCR患者通过病历审查进行验证，从而获得有效且可证明精确的 研究期间真实复发计数的估计值，无错误分类偏倚。

Project Summary/Abstract The monitoring of disease prevalence and estimation of the number of affected individuals in a defined population are among the crucial goals of epidemiologic surveillance for chronic and infectious diseases. This proposal aims to provide novel and reliable statistical tools to improve best practices for design and analysis of such surveillance studies. We take specific motivation from timely challenges associated with the registry- based monitoring of cancer recurrences in the state of Georgia Cancer Registry (GCR). We focus on customizing capture-recapture (C-R) methods, which are ever increasingly used tools for estimating total numbers of cases or deaths based on multiple epidemiologic surveillance streams. We clarify underappreciated pitfalls associated with widely popular log-linear model-based C-R techniques, and propose an accessible approach to sensitivity analysis with data visualization that promotes a general strategy for more appropriate propagation of uncertainty into ultimate estimates of case totals. This in turn provides a gateway to a broad class of useful models, whereby practitioners can transparently encode assumptions about how surveillance streams operate relative to one another at the population level. As a next step, we consider the case in which one surveillance stream is implemented by means of a well-controlled sampling design. Under appropriate conditions, this provides what we refer to as an “anchor stream”, whereby otherwise ever-present inherent uncertainties in specifying a defensible C-R model are overcome. In this setting, we will promote best statistical practices for estimating case totals by means of a novel C-R estimator that harnesses the power of the principled sampling behind the anchor stream while offering markedly enhanced precision. We propose to extend this approach to account for misclassification, which is inevitable in the case of our motivating study of cancer recurrence and in any setting in which surveillance streams identify cases in an error-prone manner. We will tailor proposed methodology toward breast and colorectal cancer recurrence monitoring via the ongoing Cancer Recurrence Information and Surveillance Program (CRISP), based on the GCR. CRISP is actively compiling informative but potentially false-positive recurrence signals from up to 6 data streams, and conducts validation sampling through protocol-based medical record review to confirm true cases among signaled recurrences. We will use such validation data to adjust for misclassification in estimating C-R-based recurrence counts. In particular, the current project will implement a principled “anchor stream” random sample of 200 GCR patients for validation through medical record review, leading to valid and demonstrably precise estimates of true recurrence counts over the study period that are free of misclassification bias.