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Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials
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 shoul...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303654/ https://www.ncbi.nlm.nih.gov/pubmed/35023184 http://dx.doi.org/10.1002/sim.9288 |
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author | Kunzmann, Kevin Grayling, Michael J. Lee, Kim May Robertson, David S. Rufibach, Kaspar Wason, James M. S. |
author_facet | Kunzmann, Kevin Grayling, Michael J. Lee, Kim May Robertson, David S. Rufibach, Kaspar Wason, James M. S. |
author_sort | Kunzmann, Kevin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9303654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93036542022-07-28 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. Stat Med Research Articles 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. John Wiley and Sons Inc. 2022-01-13 2022-02-28 /pmc/articles/PMC9303654/ /pubmed/35023184 http://dx.doi.org/10.1002/sim.9288 Text en © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Kunzmann, Kevin Grayling, Michael J. Lee, Kim May Robertson, David S. Rufibach, Kaspar Wason, James M. S. Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
title | Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
title_full | Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
title_fullStr | Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
title_full_unstemmed | Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
title_short | Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
title_sort | conditional power and friends: the why and how of (un)planned, unblinded sample size recalculations in confirmatory trials |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303654/ https://www.ncbi.nlm.nih.gov/pubmed/35023184 http://dx.doi.org/10.1002/sim.9288 |
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