Extending computations for disparity testing when data sources are uncertain

作     者：McDonald, Gary C. Oakley, Rachel H. 

作者机构：Oakland Univ Dept Math & Stat Rochester MI 48063 USA 

出 版 物：《HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY》 (保健服务和成果研究方法论)

年 卷 期：2023年第23卷第2期

页      面：207-226页

学科分类：12[管理学] 1204[管理学-公共管理] 120402[管理学-社会医学与卫生事业管理(可授管理学、医学学位)] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 10[医学] 

主　　题：Bayesian improved surname and geocoding (BISG) Laplace method Posterior distribution Metropolis-Hastings algorithms Random walk chain Independence chain Gibbs sampling WinBUGS 

摘      要：The topic of this article is one-sided hypothesis testing on the means of two populations when there is uncertainty as to which population a datum is drawn. Along with each datum, a probability is given as to which of the populations the datum emanated. Such situations arise, for example, in the use of Bayesian imputation methods to assess racial and ethnic disparities with certain survey, health, and financial data. By use of a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the population means, the probability of a disparity hypothesis is estimated. This approach extends sample size limitations of previous methods given in the literature from a few dozen to well into the thousands. Four methods are developed and compared. Three methods are implemented in R codes and one method in WinBUGS. All the codes are provided in the appendices.