title: Bayesian inference on the patient population size given list mismatches.
abstract: In applying capture-recapture methods for closed populations to epidemiology, one needs to estimate the total number of people with a certain disease in a certain research area by using several lists with information of patients. Problems of lists error often arise due to mistyping or misinformation. Adopting the concept of tag-loss methodology in animal populations, Seber et al. (Biometrics 2000; 56:1227-1232) proposed solutions to a two-list problem. This article reports an interesting simulation study, where Bayesian point estimates based on improper constant and Jeffreys prior for unknown population size N could have smaller frequentist standard errors and MSEs compared to the estimates proposed in Seber et al. (2000). The Bayesian credible intervals based on the same priors also have super frequentist coverage probabilities while some of the frequentist confidence intervals procedures have drastically poor coverage. Seber's real data set on gestational diabetics is analysed with the proposed new methods.
authors.affiliation: Mathematics Department, Towson University, Towson, MD 21252, USA. xwang@towson.edu
authors.forename: Xiaoyin
authors.initials: X
authors.lastname: Wang
authors.forename: Chong Z
authors.initials: CZ
authors.lastname: He
authors.forename: Dongchu
authors.initials: D
authors.lastname: Sun
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D016640:Diabetes, Gestational / Q000453:epidemiology; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D009520:New Zealand / Q000453:epidemiology; D011156:Population Density; D011247:Pregnancy