Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals

• Type of Material: Article
• Authors: Lin Ge, Emory University; Yuzi Zhang, Emory University; Kevin C. Ward, Emory University; Timothy L Lash, Emory University; Lance Waller, Emory University; Robert Lyles, Emory University
• Language: English
• Date: 2023-05-09
• Publisher: John Wiley & Sons Ltd
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2023 John Wiley & Sons Ltd.
• Final Published Version (URL): https://onlinelibrary.wiley.com/doi/10.1002/sim.9759
• Title of Journal or Parent Work: Statistics in Medicine
• Volume: 42
• Issue: 17
• Start Page: 2928
• End Page: 2943
• Grant/Funding Information: Partial support was provided by the National Institute of Health (NIH)/National Institute of Allergy and Infectious Diseases (P30AI050409; Del Rio PI), the NIH/National Center for Advancing Translational Sciences (UL1TR002378; Taylor PI), the NIH/National Cancer Institute (R01CA234538; Ward/Lash MPIs), and the NIH/National Cancer Institute (R01CA266574; Lyles/Waller MPIs).
• Keywords: surveillance; multiple imputation; cancer recurrence; credible interval; non-representativeness; misclassification
• Research Categories: Biology, Biostatistics; Health Sciences, Public Health; Health Sciences, Epidemiology

Abstract

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed “anchor stream” sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.