TY - JOUR
T1 - Sensitivity analysis for nonignorable missingness and outcome misclassification from proxy reports
AU - Shardell, Michelle
AU - Simonsick, Eleanor M.
AU - Hicks, Gregory E.
AU - Resnick, Barbara
AU - Ferrucci, Luigi
AU - Magaziner, Jay
PY - 2013/3
Y1 - 2013/3
N2 - Researchers often recruit proxy respondents, such as relatives or caregivers, for epidemiologic studies of older adults when study participants are unable to provide self-reports (eg, because of illness or cognitive impairment). In most studies involving proxy-reported outcomes, proxies are recruited only to report on behalf of participants who have missing self-reported outcomes; thus, either a proxy report or participant self-report, but not both, is available for each participant. When outcomes are binary and investigators conceptualize participant self-reports as gold standard measures, substituting proxy reports in place of missing participant self-reports in statistical analysis can introduce misclassification error and lead to biased parameter estimates. However, excluding observations from participants with missing self-reported outcomes may also lead to bias. We propose a pattern-mixture model that uses error-prone proxy reports to reduce selection bias from missing outcomes, and we describe a sensitivity analysis to address bias from differential outcome misclassification. We perform model estimation with high-dimensional (eg, continuous) covariates using propensity-score stratification and multiple imputation. We apply the methods to the Second Cohort of the Baltimore Hip Studies, a study of elderly hip fracture patients, to assess the relation between type of surgical treatment and perceived physical recovery. Simulation studies show that the proposed methods perform well. We provide SAS programs in the eAppendix (http://links.lww.com/EDE/A646) to enhance the methods' accessibility.
AB - Researchers often recruit proxy respondents, such as relatives or caregivers, for epidemiologic studies of older adults when study participants are unable to provide self-reports (eg, because of illness or cognitive impairment). In most studies involving proxy-reported outcomes, proxies are recruited only to report on behalf of participants who have missing self-reported outcomes; thus, either a proxy report or participant self-report, but not both, is available for each participant. When outcomes are binary and investigators conceptualize participant self-reports as gold standard measures, substituting proxy reports in place of missing participant self-reports in statistical analysis can introduce misclassification error and lead to biased parameter estimates. However, excluding observations from participants with missing self-reported outcomes may also lead to bias. We propose a pattern-mixture model that uses error-prone proxy reports to reduce selection bias from missing outcomes, and we describe a sensitivity analysis to address bias from differential outcome misclassification. We perform model estimation with high-dimensional (eg, continuous) covariates using propensity-score stratification and multiple imputation. We apply the methods to the Second Cohort of the Baltimore Hip Studies, a study of elderly hip fracture patients, to assess the relation between type of surgical treatment and perceived physical recovery. Simulation studies show that the proposed methods perform well. We provide SAS programs in the eAppendix (http://links.lww.com/EDE/A646) to enhance the methods' accessibility.
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U2 - 10.1097/EDE.0b013e31827f4fa9
DO - 10.1097/EDE.0b013e31827f4fa9
M3 - Article
C2 - 23348065
AN - SCOPUS:84873406584
SN - 1044-3983
VL - 24
SP - 215
EP - 223
JO - Epidemiology
JF - Epidemiology
IS - 2
ER -