Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials

TY - JOUR

T1 - Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials

AU - Kunzmann, Kevin

AU - Grayling, Michael J.

AU - Lee, Kim May

AU - Robertson, David S.

AU - Rufibach, Kaspar

AU - Wason, James M. S.

N1 - Funding Information: We would like to thank the editor and an anonymous reviewer for their excellent comments which greatly improved the article. This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC‐1215‐20014). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. David S. Robertson was funded by the Biometrika Trust and the Medical Research Council under Grant MC_UU_00002/6. Funding Information: information NIHR Cambridge Biomedical Research Centre, BRC-1215-20014; Biometrika Trust and the Medical Research Council, MC_UU_00002/6We would like to thank the editor and an anonymous reviewer for their excellent comments which greatly improved the article. This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. David S. Robertson was funded by the Biometrika Trust and the Medical Research Council under Grant MC_UU_00002/6. Publisher Copyright: © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

PY - 2022/2/28

Y1 - 2022/2/28

N2 - 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.

AB - 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.

KW - RESEARCH ARTICLE

KW - RESEARCH ARTICLES

KW - adaptive design

KW - conditional power

KW - interim analysis

KW - optimal design

KW - predictive power

KW - sample size recalculation

UR - http://www.scopus.com/inward/record.url?scp=85122763853&partnerID=8YFLogxK

U2 - 10.1002/sim.9288

DO - 10.1002/sim.9288

M3 - Article

SN - 0277-6715

VL - 41

SP - 877

EP - 890

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 5

ER -