A method-of-moments estimation procedure for categorical quality-of-life data with nonignorable missingness

• 成果类型: Article
• 署名作者: Bonetti, M; Cole, BF; Gelber, RD
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Dartmouth College; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard Medical School
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669916
• 发表日期: 1999
• 页码: 1025-1034
• 关键词: cancer clinical-trials; outcome subject; nonresponse; models; regression; therapy

摘要

Quality-of-life outcomes collected during clinical trials often have considerable amounts of missing data, which, if not appropriately accounted for, may lead to bias in inferences. We introduce a method-of-moments (MM) estimating procedure for a model designed to handle nonignorable missingness arising in categorical data measured on independent populations. The missingness mechanism is assumed to be the same across the populations. We derive necessary and sufficient conditions for the identifiability of the model and fit the model to quality-of-life data collected as part of a breast cancer clinical trial. We compare the MM estimator to the maximum likelihood estimator in a simulation study.