Prospective study design and data analysis in UK Biobank

Allen, Naomi E.; Lacey, Ben; Lawlor, Deborah A.; Pell, Jill P.; Gallacher, John; Smeeth, Liam; Elliott, Paul; Matthews, Paul M.; Lyons, Ronan A; Whetton, Anthony D.; Lucassen, Anneke; Hurles, Matthew E.; Chapman, Michael; Roddam, Andrew; Fitzpatrick, Natalie; Hansell, Anna; Hardy, Rebecca; Marioni, Riccardo E.; O’Donnell, Valerie B.; Williams, Julie; Lindgren, Cecilia M.; Effingham, Mark; Sellors, Jonathan; Danesh, John; Collins, Rory

doi:10.1126/scitranslmed.adf4428

reviewScience Translational MedicineJan 10, 2024GREEN OA

Prospective study design and data analysis in UK Biobank

NENaomi E. Allen BLBen LaceyDADeborah A. LawlorJPJill P. Pell JGJohn Gallacher

University of Oxford · UK Biobank · +24 more institutions

PubMed

Indexed incrossrefpubmed

Abstract

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

Citation impact

211

total citations

FWCI: 102.83
Percentile: 100%
References: 98

Citations per year

Authors

25

NE
Naomi E. AllenCorresponding
University of Oxford, UK Biobank
BL
Ben Lacey
University of Oxford, UK Biobank
DA
Deborah A. Lawlor
At Bristol, University of Bristol, Medical Research Council
JP
Jill P. Pell
University of Glasgow
JG
John Gallacher
University of Oxford, Dementias Platform UK

Topics & keywords

Topics

Keywords

Biobank
Interpretability
Prospective cohort study
Population
Medicine
Research design
Data science
Computer science

No related works found for this paper.