Document Detail

Sensitivity analysis for nonignorable missingness and outcome misclassification from proxy reports.
MedLine Citation:
PMID:  23348065     Owner:  NLM     Status:  MEDLINE    
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 ( to enhance the methods' accessibility.
Michelle Shardell; Eleanor M Simonsick; Gregory E Hicks; Barbara Resnick; Luigi Ferrucci; Jay Magaziner
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Epidemiology (Cambridge, Mass.)     Volume:  24     ISSN:  1531-5487     ISO Abbreviation:  Epidemiology     Publication Date:  2013 Mar 
Date Detail:
Created Date:  2013-02-04     Completed Date:  2013-07-23     Revised Date:  2014-03-06    
Medline Journal Info:
Nlm Unique ID:  9009644     Medline TA:  Epidemiology     Country:  United States    
Other Details:
Languages:  eng     Pagination:  215-23     Citation Subset:  IM    
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MeSH Terms
Bias (Epidemiology)*
Cohort Studies
Epidemiologic Methods*
Hip Fractures / epidemiology
Models, Statistical
Propensity Score*
Self Report
Grant Support
K25 AG 034216/AG/NIA NIH HHS; K25 AG034216/AG/NIA NIH HHS; R01 AG 06322/AG/NIA NIH HHS; R01 AG 09902/AG/NIA NIH HHS; R01 AG018668/AG/NIA NIH HHS; R01 AG029315/AG/NIA NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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