title: Multi-list methods using incomplete lists in closed populations.
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.
authors.affiliation: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada. sutherli@stat.sfu.ca
authors.forename: Jason
authors.initials: J
authors.lastname: Sutherland
authors.forename: Carl James
authors.initials: CJ
authors.lastname: Schwarz
mesh_terms: D001699:Biometry; D003920:Diabetes Mellitus / Q000453:epidemiology; D004812:Epidemiologic Methods; D015658:HIV Infections / Q000453:epidemiology; D006801:Humans; D008722:Methods; D015233:Models, Statistical; D011153:Population; D015995:Prevalence; D019966:Substance-Related Disorders / Q000453:epidemiology