Multi-list methods using incomplete lists in closed populations.

Jason Sutherland; Carl James Schwarz

doi:10.1111/j.0006-341x.2005.021126.x

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Multi-list methods using incomplete lists in closed populations.

Jason Sutherland·Carl James Schwarz

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Abstract

Multi-list methods have become a common application of capture-recapture methodology to estimate the size of human populations, and have been successfully applied to estimating prevalence of diabetes, human immunodeficiency virus (HIV), and drug abuse. A key assumption in multi-list methods is that individuals have a unique "tag" that allows them to be matched across all lists. This article develops multi-list methodology that relaxes the assumption of a single tag common to all lists. Estimates are found using estimating functions. An example illustrates its application for estimating the prevalence of diabetes, and a simulation study investigates conditions under which the methodology is robust to different list and population sizes.

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Biometry Diabetes Mellitus Epidemiologic Methods HIV Infections Humans Methods Models, Statistical Population Prevalence Substance-Related Disorders Journal Article Research Support, Non-U.S. Gov't

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Jason Sutherland, Carl James Schwarz. 2005. Multi-list methods using incomplete lists in closed populations.. https://doi.org/10.1111/j.0006-341x.2005.021126.x

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