MULTIPLE IMPUTATION IN MIXTURE-MODELS FOR NONIGNORABLE NONRESPONSE WITH FOLLOW-UPS

• 成果类型: Article
• 署名作者: GLYNN, RJ; LAIRD, NM; RUBIN, DB
• 署名单位: Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290790
• 发表日期: 1993
• 页码: 984-993
• 关键词: selectivity bias; dependent variables; sample-surveys; estimator; inference

摘要

One approach to inference for means or linear regression parameters when the outcome is subject to nonignorable nonresponse is mixture modeling. Mixture models assume separate parameters for respondents and nonrespondents; implementation by multiple imputation consists of repeatedly filling in missing values for nonrespondents, estimating parameters using the filled-in data, and then adjusting for variability between imputations. We evaluated the performance of this scheme using simulated data with a 25% sample of nonrespondents followed up. We conclude that it provides a generally satisfactory and robust approach to inference for means and regression parameters in this case, although a greater number of imputations may be required for good performance compared to the number required for estimation when nonresponse is ignorable.