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Activity Number: 27 - Innovative Methods for Missing Data and Measurement Error in Health Research
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Health Policy Statistics Section
Abstract #309692
Title: Addressing Non-Ignorably Missing Proxy-Reported Exposure Data
Author(s): Michelle Shardell*
Companies: Institute for Genome Sciences University of Maryland School of Medicine
Keywords: misclassification; missing data; proxy data; boosted regression; propensity scores

In studies of older adults, researchers often recruit proxy respondents, such as relatives or caregivers, when study participants cannot provide self-reports (e.g., because of illness). Proxies are usually only sought to report on behalf of participants with missing self-reports; thus, either a participant self-report or proxy report, but not both, is available for each participant. Furthermore, the missing-data mechanism for participant self-reports may be nonignorable. When exposures are binary and participant self-reports are the gold standard, substituting error-prone proxy reports for missing participant self-reports may produce biased estimates of outcome means. Researchers can handle this data structure by treating the problem as one of misclassification among participants with missing self-reports. We propose to estimate outcome means standardized for high-dimensional covariates using multiple imputation with propensity score methods. This nonparametric method uses boosted classification and regression trees to compatibly estimate the exposure misclassification model and the propensity score model. We apply the method to a study of elderly hip fracture patients.

Authors who are presenting talks have a * after their name.

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