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Grant Details

Grant Number: 1R01CA266574-01A1 Interpret this number
Primary Investigator: Lyles, Robert
Organization: Emory University
Project Title: Refined Capture-Recapture Methods for Surveilling Cancer Recurrence
Fiscal Year: 2022


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.


A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.
Authors: Lyles R.H. , Zhang Y. , Ge L. , Waller L.A. .
Source: American journal of epidemiology, 2024-01-08; 193(1), p. 193-202.
PMID: 37625449
Related Citations

Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors.
Authors: Ge L. , Zhang Y. , Waller L.A. , Lyles R.H. .
Source: The American statistician, 2024; 78(2), p. 192-198.
EPub date: 2023-09-21.
PMID: 38645436
Related Citations

Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals.
Authors: Ge L. , Zhang Y. , Ward K.C. , Lash T.L. , Waller L.A. , Lyles R.H. .
Source: Statistics in medicine, 2023-07-30; 42(17), p. 2928-2943.
EPub date: 2023-05-09.
PMID: 37158167
Related Citations

Using Capture-Recapture Methodology to Enhance Precision of Representative Sampling-Based Case Count Estimates.
Authors: Lyles R.H. , Zhang Y. , Ge L. , England C. , Ward K. , Lash T.L. , Waller L.A. .
Source: Journal of survey statistics and methodology, 2022 Nov; 10(5), p. 1292-1318.
EPub date: 2022-01-05.
PMID: 36397765
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