title: Estimating sample sizes for continuous, binary, and ordinal outcomes in paired comparisons: practical hints.
abstract: Paired data occur in crossover trials and matched case-control studies, and it is rare to find studies reporting sample size calculations associated with these types of studies, despite recommendations from editors that sample size calculations should be justified. In this article we describe some simple formulas and strategies for calculating the number of patients that should be entered into a matched or paired study when the outcome measures are continuous, binary, or ordinal.
authors.affiliation: Clinical Pharmacology Data Sciences, Glaxo Wellcome, Greenford, London, UK.
authors.forename: S A
authors.initials: SA
authors.lastname: Julious
authors.forename: M J
authors.initials: MJ
authors.lastname: Campbell
authors.forename: D G
authors.initials: DG
authors.lastname: Altman
mesh_terms: D000368:Aged; D000924:Anticholesteremic Agents / Q000627:therapeutic use; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D002784:Cholesterol / Q000097:blood; D002986:Clinical Trials as Topic / Q000379:methods; D018592:Cross-Over Studies; D005194:Family Practice; D005260:Female; D006801:Humans; D008297:Male; D009819:Office Visits; D018401:Sample Size; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D000924:Anticholesteremic Agents; D002784:Cholesterol