title: Continuous endpoints in Bayesian two-stage designs.
abstract: Single-arm two-stage designs for phase II of clinical trials typically focus on a binary endpoint obtained by dichotomizing an underlying continuous measure of treatment efficacy. To avoid the resulting loss of information, we propose a two-stage design based on a Bayesian predictive approach that directly uses the original continuous endpoint. Numerical results are provided with reference to phase II cancer trials aimed at assessing tumor shrinking effect of an experimental treatment.
authors.affiliation: a Dipartimento di Scienze Statistiche , Sapienza Università di Roma , Rome , Italy.
authors.forename: Pierpaolo
authors.initials: P
authors.lastname: Brutti
authors.affiliation: a Dipartimento di Scienze Statistiche , Sapienza Università di Roma , Rome , Italy.
authors.forename: Fulvio
authors.initials: F
authors.lastname: De Santis
authors.affiliation: a Dipartimento di Scienze Statistiche , Sapienza Università di Roma , Rome , Italy.
authors.forename: Stefania
authors.initials: S
authors.lastname: Gubbiotti
authors.affiliation: a Dipartimento di Scienze Statistiche , Sapienza Università di Roma , Rome , Italy.
authors.forename: Valeria
authors.initials: V
authors.lastname: Sambucini
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic; D023381:Endpoint Determination; D006801:Humans; D009369:Neoplasms / Q000628:therapy; D012107:Research Design; D016896:Treatment Outcome
keywords: Analysis prior; Design prior; Sample size determination; Skew normal distribution