Evidence map›Paper›PMID 31211407›Full record

ReviewJournal of the American Geriatrics Society2019

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

Abstract readReview
In one paragraph

Review in Journal of the American Geriatrics Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

60 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Midlife hypertension and late-life cognition: weighting the LifeAfter90 study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Observational
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hailey R BanackDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York.
Jay S KaufmanDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
Jean Wactawski-WendeDepartment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York.
Bruce R TroenDivision 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.
Steven D StovitzDepartment of Family Medicine and Community Health, University of Minnesota System, Minneapolis, Minnesota.

Funding

Enhancing Geroscience Scholarship and Faculty Development to Minimize Frailty and Maximize HealthspanK07AG060266 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI TROEN, BRUCE R. · 2019 to 2023
$774k
NIA NIH HHS K07 AG060266
6 · The paper itself

Abstract

objectivesSelection 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.

designWe 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.

resultsSurvivor 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).

conclusionThe 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.

Indexed as

GeriatricsPatient SelectionAgedAged, 80 and overFemaleHumansLost to Follow-UpMaleResearch DesignSelection BiasSurvivorsloss to follow-upobesityselection biassurvivor bias

Identifiers

PMID31211407
PMCPMC9930538

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.