Prediction of Functional Status for the Elderly Based on a New Ordinal Regression Model

• 成果类型: Article
• 署名作者: Hong, Hyokyoung Grace; He, Xuming
• 署名单位: City University of New York (CUNY) System; Baruch College (CUNY); City University of New York (CUNY) System; Baruch College (CUNY); University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2010.ap08631
• 发表日期: 2010
• 页码: 930-941
• 关键词: LINEAR-REGRESSION; TRANSFORMATION; transitions; patterns

摘要

The functional mobility of the elderly is a very important factor in aging research, and prognostic information is valuable in making clinical and health care policy decisions. We develop a predictive model for the functional status of the elderly based on data from the Second Longitudinal Study of Aging (LSOA II). The functional status is an ordinal response variable. The ordered probit model has been moderately successful in analyzing such data; however, its reliance on the normal distribution for its latent variable hinders its accuracy and potential. In this paper, we focus on the prediction of conditional quantiles of the functional status based on a more general transformation model. The proposed estimation procedure does not rely on any parametric specification of the conditional distribution functions, aiming to reduce model misspecification errors in the prediction. Cross-validation within the LSOA II data shows that our prediction intervals are more informative than those from the ordered probit model. Monte Carlo simulations also demonstrate the merits of our approach in the analysis of ordinal response variables.