Sensitivity analysis for nonignorable missingness and outcome misclassification from proxy reports.
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 recruite...
| Publicado en: | Epidemiology Vol. 24; no. 2; pp. 215 - 224 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2013 Mar
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=108025222&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 108025222 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10443983 N07 jtl: Epidemiology issn: 10443983 maglogo: N pubinfo: dt: 2013 Mar vid: 24 iid: 2 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 108025222 108025222 NLM23348065 2011920151 10.1097/EDE.0b013e31827f4fa9 NLM23348065 PMC3566762 108025222 ppf: 215 ppct: 9 formats: tig: atl: Sensitivity analysis for nonignorable missingness and outcome misclassification from proxy reports. aug: au: Shardell M Simonsick EM Hicks GE Resnick B Ferrucci L Magaziner J Shardell, Michelle Simonsick, Eleanor M Hicks, Gregory E Resnick, Barbara Ferrucci, Luigi Magaziner, Jay affil: Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA sug: subj: Bias (Research) Epidemiological Research Probability Guardianship, Legal Maryland Prospective Studies Hip Fractures Epidemiology Human Models, Statistical Self Report 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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