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.

项目概要/摘要 监测疾病流行情况并估计一定范围内受影响的人数 人口是慢性病和传染病流行病学监测的关键目标之一。 该提案旨在提供新颖且可靠的统计工具，以改进设计和分析的最佳实践 我们从与登记相关的及时挑战中获取特定的动机。 基于乔治亚州癌症登记处 (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 我们根据多个流行病学监测流估计病例或死亡总数。 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 一种通过数据可视化进行敏感性分析的易于使用的方法，可促进更多的总体策略 将不确定性适当传播到病例总数的最终估计中，这反过来又提供了一个途径。 一类广泛的有用模型，任何人都可以透明地编码有关如何进行的假设 监测流在人口层面上相互关联 下一步，我们考虑 通过良好控制的抽样设计实施一个监测流的情况。 适当的条件下，这提供了我们所说的“锚流”，因此否则永远存在 在这种情况下，我们将克服指定可防御的 C-R 模型的固有不确定性。 通过一种新颖的 C-R 估计器来估计病例总数的统计实践，该估计器利用了 我们建议在锚流后面进行采样，同时提供显着提高的精度。 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 癌症复发以及监测流以容易出错的方式识别病例的任何环境中。 我们将通过以下方法定制乳腺癌和结直肠癌复发监测的拟议方法： 正在进行的癌症复发信息和监测计划 (CRISP)，以 GCR 为基础。 从多达 6 个数据流中主动编译信息丰富但可能误报的复发信号，以及 通过基于协议的医疗记录审查进行验证抽样，以确认真实病例 我们将使用此类验证数据来调整基于 C-R 的估计中的错误分类。 特别是，当前项目将实施有原则的“锚流”随机样本。 通过病历审查对 200 名 GCR 患者进行验证，从而得出有效且精确的结果 对研究期间真实复发计数的估计，不存在错误分类偏差。