title: Determination of the optimal sample size for a clinical trial accounting for the population size.
abstract: The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflect the size of the population under consideration. Incorporation of the population size is possible in a decision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N1/2) or O(N∗1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes.
authors.affiliation: Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
authors.forename: Nigel
authors.initials: N
authors.lastname: Stallard
authors.affiliation: Department of Statistics, Stockholm University, Stockholm, Sweden.
authors.forename: Frank
authors.initials: F
authors.lastname: Miller
authors.affiliation: Clinical Trials Consulting and Training Limited, Buckingham, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: Day
authors.affiliation: Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
authors.forename: Siew Wan
authors.initials: SW
authors.lastname: Hee
authors.affiliation: Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.
authors.forename: Jason
authors.initials: J
authors.lastname: Madan
authors.affiliation: INSERM, U1138, team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France.
authors.forename: Sarah
authors.initials: S
authors.lastname: Zohar
authors.affiliation: Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Austria.
authors.forename: Martin
authors.initials: M
authors.lastname: Posch
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D016012:Poisson Distribution; D011156:Population Density; D018401:Sample Size
keywords: Bayesian; Clinical trial design; Decision theory; Exponential family form; Optimal sample size