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J Am Geriatr Soc. 2019 Jun 18. doi: 10.1111/jgs.16022. [Epub ahead of print]

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

Author information

1
Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York.
2
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
3
Division of Geriatrics and Palliative Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo and Research Service, Veterans Affairs Western New York Healthcare System, Buffalo, New York.
4
Department of Family Medicine and Community Health, University of Minnesota System, Minneapolis, Minnesota.

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.

KEYWORDS:

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

PMID:
31211407
DOI:
10.1111/jgs.16022

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