title: Sample size estimation for repeated measures analysis in randomized clinical trials with missing data.
abstract: In designing longitudinal studies, researchers must determine the number of subjects to randomize based on the power to detect a clinically meaningful treatment difference and a proposed analysis plan. In this paper, we present formulas for sample size estimation and an assessment of statistical power for a two-treatment repeated measures design allowing for subject attrition. These formulas can be used for comparing two treatment groups across time in terms of linear contrasts. Subjects are assumed to drop out of the study at random so that the missing data do not alter the parameters of interest.
authors.affiliation: Merck & Co.
authors.forename: Kaifeng
authors.initials: K
authors.lastname: Lu
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Luo
authors.forename: Pei-Yun
authors.initials: PY
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D056808:Biostatistics / Q000379:methods*; D015519:Bone Density; D002136:Calcium, Dietary / Q000008:administration & dosage; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018401:Sample Size
chemical_list: D002136:Calcium, Dietary