Statistical Methods to Correct for Proxy Bias in Studies of Older Adults

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DESCRIPTION (provided by applicant): This proposal has two goals: 1) to develop Dr. Shardell, a biostatistician, into an independent investigator performing research at the interface of biostatistics and gerontology; and 2) to develop statistical methods needed to more appropriately use data from proxy respondents (e.g., relatives or care givers) in epidemiological studies of elderly hip-fracture patients. Regarding the latter goal, a significant problem in epidemiologic studies of hip fracture patients is selection bias due to the large amount of data missing from the most frail and cognitively impaired patients, which may lead to biased results and inaccurate study conclusions. Currently, in order to minimize selection bias due to missing data, proxies are recruited to supply responses in place of patients who are unable or unwilling to respond to interview questions. However, responses from proxies may be systematically biased. This bias is a significant problem because it implies that the standard statistical approach of imputing missing patient data with responses from proxies can lead to inaccurate study conclusions. Therefore, bias from these proxies can impede investigators' ability to accurately identify promising targets of intervention that may improve patients' post-fracture prognosis. To solve this problem, statistical methods originally designed to adjust for selection bias from missing data will be extended to include proxy data. Availability of statistical methods that can correct for proxy bias can help in designing accurately-targeted interventions for postfracture recovery. Computer programs for the methods will be made available online for use by the gerontology community. The new approaches will be validated and compared as part of a future R01 proposal. The three-year mentored research program described in this proposal involves a career development plan that includes coursework in the biology, sociology, and psychosocial aspects of aging; regular meetings with mentors; participation at seminars, workshops, and professional conferences; and exposure to clinical geriatrics settings by shadowing clinician-researchers. Trained investigators with expertise in both biostatistics and gerontology are urgently needed to solve problems in study design, outcome measurement, and statistics that are relevant to aging research. PUBLIC HEALTH RELEVANCE: Accurately identifying behaviors and other targets of intervention that can improve physical and emotional functioning and protect against age-related diseases is the primary goal of gerontological research. Effective policy for care of older adults requires the ability to determine factors that can improve prognosis. Current statistical methods used to inform these decisions rely on imputing missing patient data with proxy data, methods which are known to lead to biased results. Improved statistical methods will allow correction for proxy bias to improve the accuracy of results, leading to more accurate conclusions regarding the effectiveness of interventions on health outcomes.