DEVELOPMENT OF A COMMON PATIENT ASSESSMENT SCALE ACROSS THE CONTINUUM OF CARE: A NESTED MULTIPLE IMPUTATION APPROACH

• 成果类型: Article
• 署名作者: Gu, Chenyang; Gutman, Roee
• 署名单位: Harvard University; Brown University; Harvard University; Harvard Medical School
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/18-AOAS1202
• 发表日期: 2019
• 页码: 466-491
• 关键词: maximum-likelihood-estimation; categorical variables; parameter expansion; bayesian-inference; models; binary; em; algorithm

摘要

Evaluating and tracking patients' functional status through the post-acute care continuum requires a common instrument. However, different post-acute service providers such as nursing homes, inpatient rehabilitation facilities and home health agencies rely on different instruments to evaluate patients' functional status. These instruments assess similar functional status domains, but they comprise different activities, rating scales and scoring instructions. These differences hinder the comparison of patients' assessments across health care settings. We propose a two-step procedure that combines nested multiple imputation with the multivariate ordinal probit (MVOP) model to obtain a common patient assessment scale across the post-acute care continuum. Our procedure imputes the unmeasured assessments at multiple assessment dates and enables evaluation and comparison of the rates of functional improvement experienced by patients treated in different health care settings using a common measure. To generate multiple imputations of the unmeasured assessments using the MVOP model, a likelihood-based approach that combines the EM algorithm and the bootstrap method as well as a fully Bayesian approach using the data augmentation algorithm are developed. Using a dataset on patients who suffered a stroke, we simulate missing assessments and compare the MVOP model to existing methods for imputing incomplete multivariate ordinal variables. We show that, for all of the estimands considered, and in most of the experimental conditions that were examined, the MVOP model appears to be superior. The proposed procedure is then applied to patients who suffered a stroke and were released from rehabilitation facilities either to skilled nursing facilities or to their homes.