Refined Capture-Recapture Methods for Surveilling Cancer Recurrence

用于监测癌症复发的精细捕获-再捕获方法

• 批准号: 10522710
• 负责人: Robert H Lyles
• 金额: $ 38.08万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522710
• 关键词: Address; Affect; Attention; Automobile Driving; Breast; Breast Cancer Patient; Cardiovascular Diseases; Cessation of life; Characteristics; Chronic Disease; Code; Colorectal; Colorectal Cancer; Communicable Diseases; Computer software; Crystallization; Custom; Data; Data Sources; Development; Diagnostic; Disease; Disease Surveillance; Ensure; Epidemiologic Monitoring; Epidemiologist; Epidemiology; Funding; Future; Goals; HIV Infections; Hepatitis C; Individual; Intuition; Log-Linear Models; Malignant Neoplasms; Manufacturer Name; Medical Records; Methodology; Methods; Modeling; Monitor; Monitoring for Recurrence; Motivation; Pathway interactions; Patients; Performance; Population; Predictive Value; Prevalence; Property; Protocols documentation; Recurrence; Registries; Reproducibility; Research Design; Research Personnel; Research Project Grants; Sampling; Sensitivity and Specificity; Signal Transduction; Specific qualifier value; Statistical Methods; Stream; Surveillance Program; System; Techniques; Time; Training; Tuberculosis; Uncertainty; Validation; Viral; analytical method; base; cancer recurrence; comparative; data streams; data visualization; design; disease registry; flexibility; follow-up; improved; malignant breast neoplasm; mortality; multiple data sources; neoplasm registry; novel; pathogen; patient registry; simulation; surveillance data; surveillance study; theories; tool

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.

项目摘要/摘要 监测疾病患病率和估计被定义的受影响个体的数量 人口是慢性和传染病的流行病学监测的关键目标之一。这 建议旨在提供新颖而可靠的统计工具，以改善设计和分析的最佳实践 这样的监视研究。我们从与注册表相关的及时挑战中采取具体动机 - 基于佐治亚州癌症登记处（GCR）的癌症回报的基于监测。 我们专注于自定义捕获征收（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的总数。我们澄清 与广泛流行的基于对数线模型的C-R技术相关的陷阱不足，建议 通过数据可视化的一种可访问的敏感性分析方法，可促进一般策略以获得更多 将不确定性的适当传播到案件总数的最终估计中。反过来，这为通往的门户提供了 一系列有用的模型，从业人员可以透明地编码有关如何的假设 监视流在人口水平相对运行。作为下一步，我们考虑 通过良好控制的采样设计实现了一个监视流的情况。在下面 适当的条件，这提供了我们所说的“锚流” 克服了指定可辩护的C-R模型的固有不确定性。在这种情况下，我们将最好 通过新颖的C-R估计器估算案例总数的统计实践，该估计器利用 锚点后面的校长采样显着提高了精度。我们建议 扩展这种方法以说明错误分类，这是我们激励研究的情况 癌症复发，在任何情况下，监视流以错误的方式识别病例。 我们将通过 持续的癌症复发信息和监视计划（CRISP），基于GCR。酥脆 积极地收集信息丰富但潜在的假阳性复发信号，最多6个数据流，以及 通过基于协议的医疗记录审查进行验证采样，以确认 信号回报。我们将使用此类验证数据来调整错误分类，以估算基于C-R的错误 复发数。特别是，当前项目将实施一个主要的“锚流”随机样本 通过医疗记录审查验证的200名GCR患者，导致有效和证明精度 在研究期间，真正复发的估计值没有错误分类偏差。