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Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials.
Kunzmann, Kevin; Grayling, Michael J; Lee, Kim May; Robertson, David S; Rufibach, Kaspar; Wason, James M S.
Afiliação
  • Kunzmann K; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Grayling MJ; Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
  • Lee KM; Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
  • Robertson DS; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Rufibach K; Methods, Collaboration, and Outreach Group (MCO), Product Development Data Sciences, F. Hoffmann-La Roche, Basel, Switzerland.
  • Wason JMS; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Stat Med ; 41(5): 877-890, 2022 02 28.
Article em En | MEDLINE | ID: mdl-35023184
ABSTRACT
Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Since the sample size is usually determined by an argument based on the power of the trial, an interim analysis raises the question of how the final sample size should be determined conditional on the accrued information. To this end, we first review and compare common approaches to estimating conditional power, which is often used in heuristic sample size recalculation rules. We then discuss the connection of heuristic sample size recalculation and optimal two-stage designs, demonstrating that the latter is the superior approach in a fully preplanned setting. Hence, unplanned design adaptations should only be conducted as reaction to trial-external new evidence, operational needs to violate the originally chosen design, or post hoc changes in the optimality criterion but not as a reaction to trial-internal data. We are able to show that commonly discussed sample size recalculation rules lead to paradoxical adaptations where an initially planned optimal design is not invariant under the adaptation rule even if the planning assumptions do not change. Finally, we propose two alternative ways of reacting to newly emerging trial-external evidence in ways that are consistent with the originally planned design to avoid such inconsistencies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Amigos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Amigos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article