Investigating and Remediating Selection Bias in Geriatrics Research: The Selection Bias Toolkit

Hailey R. Banack, Jay S. Kaufman, Jean Wactawski-Wende, Bruce R. Troen, Steven D. Stovitz

Research output: Contribution to journalReview article

Abstract

OBJECTIVES: Selection bias is a well-known concern in research on older adults. We discuss two common forms of selection bias in aging research: (1) survivor bias and (2) bias due to loss to follow-up. Our objective was to review these two forms of selection bias in geriatrics research. In clinical aging research, selection bias is a particular concern because all participants must have survived to old age, and be healthy enough, to take part in a research study in geriatrics. DESIGN: We demonstrate the key issues related to selection bias using three case studies focused on obesity, a common clinical risk factor in older adults. We also created a Selection Bias Toolkit that includes strategies to prevent selection bias when designing a research study in older adults and analytic techniques that can be used to examine, and correct for, the influence of selection bias in geriatrics research. RESULTS: Survivor bias and bias due to loss to follow-up can distort study results in geriatric populations. Key steps to avoid selection bias at the study design stage include creating causal diagrams, minimizing barriers to participation, and measuring variables that predict loss to follow-up. The Selection Bias Toolkit details several analytic strategies available to geriatrics researchers to examine and correct for selection bias (eg, regression modeling and sensitivity analysis). CONCLUSION: The toolkit is designed to provide a broad overview of methods available to examine and correct for selection bias. It is specifically intended for use in the context of aging research. J Am Geriatr Soc 67:1970–1976, 2019.

Original languageEnglish (US)
Pages (from-to)1970-1976
Number of pages7
JournalJournal of the American Geriatrics Society
Volume67
Issue number9
DOIs
StatePublished - Sep 1 2019

    Fingerprint

Keywords

  • loss to follow-up
  • obesity
  • selection bias
  • survivor bias

Cite this