Evidence map›Paper›PMID 31859414›Full record

ArticleStatistics in medicine2020

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.

Lauren J Beesley, Maxwell Salvatore, Lars G Fritsche, Anita Pandit, Arvind Rao, Chad Brummett, Cristen J Willer, Lynda D Lisabeth, Bhramar Mukherjee

Open access · hybridAbstract read
In one paragraph

Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 2 pooled it
6.7field-weighted citation impact, top 3% of its field
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

63 citing papers in PubMed, 2 syntheses or guidelines pooled it, 101 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. The case for an integrated biobanking initiative in South Asia.The Lancet regional health. Southeast Asia · 2026
    Review
  6. Article
  7. Article
  8. Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Revisiting representativeness.International journal of epidemiology · 2025
    Article
  19. Systematic method for classifying multiple congenital anomaly cases in electronic health records.Genetics in medicine : official journal of the American College of Medical Genetics · 2025
    Article
  20. SPANature communications · 2025
    Article

3 more citing papers are in PubMed but not listed here.

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

9 authors at 1 institution in 1 country.

Lauren J BeesleyDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0002-3788-5944
Maxwell SalvatoreDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0002-3659-1514
Lars G FritscheDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Anita PanditDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.
Arvind RaoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.
Chad BrummettDepartment of Anesthesiology, University of Michigan, Ann Arbor, Michigan.
Cristen J WillerDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.
Lynda D LisabethDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan.
University of Michigan · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
University of Michigan ERC-supplement (TRT)T42OH008455 · OH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NEITZEL, RICHARD L · 2005 to 2025
$32.3M
Medical Research Council MC_PC_12028Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCI NIH HHS P30 CA046592NIOSH CDC HHS T42 OH008455
6 · The paper itself

Abstract

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

Indexed as

Biological Specimen BanksElectronic Health RecordsGenomicsMichiganResearch Designbiobankselectronic health recordsMichigan Genomics Initiativeselection biasUK Biobank

Identifiers

PMID31859414
PMCPMC7983809
OpenAlexW2994642650

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.