Mann-Whitney test with adjustments to pretreatment variables for missing values and observational study

• 成果类型: Article
• 署名作者: Chen, Song Xi; Qin, Jing; Tang, Cheng Yong
• 署名单位: Peking University; Iowa State University; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National University of Singapore; University of Colorado System; University of Colorado Denver
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2012.01036.x
• 发表日期: 2013
• 页码: 81-102
• 关键词: nonparametric-estimation; regression; efficient; inference

摘要

The conventional Wilcoxon or Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcomes or the participation in the treatments may depend on certain pretreatment variables. We propose an approach to adjust the Mann-Whitney test by correcting the potential bias via consistently estimating the conditional distributions of the outcomes given the pretreatment variables. We also propose semiparametric extensions of the adjusted Mann-Whitney test which lead to dimension reduction for high dimensional covariates. A novel boot-strap procedure is devised to approximate the null distribution of the test statistics for practical implementations. Results from simulation studies and an economics observational study data analysis are presented to demonstrate the performance of the approach proposed.