title: Statistical Considerations of Food Allergy Prevention Studies.
abstract: Clinical studies to prevent the development of food allergy have recently helped reshape public policy recommendations on the early introduction of allergenic foods. These trials are also prompting new research, and it is therefore important to address the unique design and analysis challenges of prevention trials. We highlight statistical concepts and give recommendations that clinical researchers may wish to adopt when designing future study protocols and analysis plans for prevention studies. Topics include selecting a study sample, addressing internal and external validity, improving statistical power, choosing alpha and beta, analysis innovations to address dilution effects, and analysis methods to deal with poor compliance, dropout, and missing data.
authors.affiliation: Immune Tolerance Network, Benaroya Research Institute, Seattle, Wash.
authors.forename: Henry T
authors.initials: HT
authors.lastname: Bahnson
authors.affiliation: Department of Pediatric Allergy, Division of Asthma, Allergy and Lung Biology, King's College London, London, United Kingdom; Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
authors.forename: George
authors.initials: G
authors.lastname: du Toit
authors.affiliation: Department of Pediatric Allergy, Division of Asthma, Allergy and Lung Biology, King's College London, London, United Kingdom; Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom. Electronic address: Gideon.Lack@kcl.ac.uk.
authors.forename: Gideon
authors.initials: G
authors.lastname: Lack
mesh_terms: D000485:Allergens / Q000276:immunology*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D005512:Food Hypersensitivity / Q000517:prevention & control*; D006801:Humans; D010349:Patient Compliance; D011640:Public Policy; D012107:Research Design / Q000706:statistics & numerical data
chemical_list: D000485:Allergens
keywords: Complier average causal effect; Dilution effects; Dropout; EAT; Food allergy; Imputation; LEAP; Missing data; Prevention studies; Statistical considerations; Tipping point analysis; Type I error; Type II error