title: A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints.
abstract: The clinical efficacy of a new treatment may often be better evaluated by two or more co-primary endpoints. Recently, in pharmaceutical drug development, there has been increasing discussion regarding establishing statistically significant favorable results on more than one endpoint in comparisons between treatments, which is referred to as a problem of multiple co-primary endpoints. Several methods have been proposed for calculating the sample size required to design a trial with multiple co-primary correlated endpoints. However, because these methods require users to have considerable mathematical sophistication and knowledge of programming techniques, their application and spread may be restricted in practice. To improve the convenience of these methods, in this paper, we provide a useful formula with accompanying numerical tables for sample size calculations to design clinical trials with two treatments, where the efficacy of a new treatment is demonstrated on continuous co-primary endpoints. In addition, we provide some examples to illustrate the sample size calculations made using the formula. Using the formula and the tables, which can be read according to the patterns of correlations and effect size ratios expected in multiple co-primary endpoints, makes it convenient to evaluate the required sample size promptly.
authors.affiliation: Department of Mathematical Sciences, Hirosaki University Graduate School of Science and Technology, 3 Bunkyo-cho, Hirosaki, Aomori 036-8561, Japan. tomoyuki@cc.hirosaki-u.ac.jp
authors.forename: Tomoyuki
authors.initials: T
authors.lastname: Sugimoto
authors.forename: Takashi
authors.initials: T
authors.lastname: Sozu
authors.forename: Toshimitsu
authors.initials: T
authors.lastname: Hamasaki
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D015195:Drug Design; D023381:Endpoint Determination; D006801:Humans; D012107:Research Design; D018401:Sample Size