Determination of the optimal sample size for a clinical trial accounting for the population size
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TLDR
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.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.read more
Citations
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Journal ArticleDOI
DELTA2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial
Jonathan Cook,Steven A. Julious,William Sones,Lisa V. Hampson,Lisa V. Hampson,Catherine Hewitt,Jesse A. Berlin,Deborah Ashby,Richard Emsley,Dean Fergusson,Stephen J Walters,Edward C. F. Wilson,Graeme MacLennan,Nigel Stallard,Joanne C. Rothwell,Martin Bland,Louise Brown,Craig R Ramsay,Andrew Cook,David Armstrong,Doug G Altman,Luke Vale +21 more
TL;DR: The DELTA2 (Difference ELicitation in TriAls) project as discussed by the authors provides guidance for researchers and funders on specifying the target difference, and undertaking and reporting a RCT sample size calculation.
Journal ArticleDOI
DELTA2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial
Jonathan Cook,Steven A. Julious,William Sones,Lisa V. Hampson,Lisa V. Hampson,Catherine Hewitt,Jesse A. Berlin,Deborah Ashby,Richard Emsley,Dean Fergusson,Stephen J Walters,Edward C. F. Wilson,Graeme MacLennan,Nigel Stallard,Joanne C. Rothwell,Martin Bland,Louise Brown,Craig R Ramsay,Andrew Cook,David Armstrong,Doug G Altman,Luke Vale +21 more
TL;DR: In this paper, the DELTA2 guidance on determining the target difference and sample size calculation for a randomised controlled trial is presented, along with recommendations for subsequent reporting of the sample size calculations.
Journal ArticleDOI
A Bayesian approach to the design of phase II clinical trials
TL;DR: The decision theoretic/Bayesian approach is shown to provide a formal justification for the sample sizes often used in practice and shows the conditions under which such sample sizes are clearly inappropriate.
Journal ArticleDOI
Recent advances in methodology for clinical trials in small populations: the InSPiRe project
Tim Friede,Martin Posch,Sarah Zohar,Corinne Alberti,Norbert Benda,Emmanuelle Comets,Emmanuelle Comets,Simon Day,Alex Dmitrienko,Alexandra Graf,Burak Kürsad Günhan,Siew Wan Hee,Frederike Lentz,Jason Madan,Frank Miller,Thomas Ondra,Michael Pearce,Christian Röver,Artemis Toumazi,Steffen Unkel,Moreno Ursino,Gernot Wassmer,Nigel Stallard +22 more
TL;DR: The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas, including new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework.
Journal ArticleDOI
Recent advances in methodology for clinical trials in small populations: the InSPiRe project
Tim Friede,Martin Posch,Sarah Zohar,Corinne Alberti,Norbert Benda,Emmanuelle Comets,Simon Day,A. Dmitrenko,Alexandra Graf,Burak Kürsad Günhan,Siew Wan Hee,Frederike Lentz,Jason Madan,Frank Miller,Thomas Ondra,Michael Pearce,Christian Röver,A. Tournazi,Steffen Unkel,Moreno Ursino,Gernot Wassmer,Nigel Stallard +21 more
TL;DR: The InSPiRe project as discussed by the authors developed decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as
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