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Conditional PowerHow Likely Is Trial Success?

To identify the key insights or developments described in this article
1 Credit CME

Because of the costs and potential risks to participants, clinical trials should be initiated or continued only when there is a reasonable chance the results will improve clinical care or represent important new knowledge. In 2021, McNamee et al1 reported the results of a randomized clinical trial evaluating the therapeutic benefit of lower tidal volume ventilation coupled with extracorporeal carbon dioxide removal compared with standard ventilatory support in patients with acute hypoxemic respiratory failure. During a pause in recruitment for investigation of a severe adverse event, the independent data monitoring and ethics committee (DMC) made a recommendation to stop the trial early for futility, based on the feasibility of future recruitment and the low likelihood of eventual trial success. The DMC used a conditional power calculation to show that even under the most optimistic assumptions of treatment benefit, the trial had only a 44% chance of demonstrating a statistically significant benefit from the intervention on 90-day all-cause mortality.1

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Article Information

Corresponding Author: Benjamin R. Saville, PhD, Berry Consultants LLC, 3345 Bee Caves Rd, Ste 201, Austin, TX 78746 (ben@berryconsultants.net).

Published Online: January 23, 2023. doi:10.1001/jama.2022.25080

Conflict of Interest Disclosures: None reported.

References
1.
McNamee  JJ , Gillies  MA , Barrett  NA ,  et al; REST Investigators.  Effect of lower tidal volume ventilation facilitated by extracorporeal carbon dioxide removal vs standard care ventilation on 90-day mortality in patients with acute hypoxemic respiratory failure: the REST randomized clinical trial.   JAMA. 2021;326(11):1013-1023. doi:10.1001/jama.2021.13374PubMedGoogle ScholarCrossref
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Stokes  L .  Sample size calculation for a hypothesis test.   JAMA. 2014;312(2):180-181. doi:10.1001/jama.2014.8295PubMedGoogle ScholarCrossref
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Lachin  JM .  A review of methods for futility stopping based on conditional power.   Stat Med. 2005;24(18):2747-2764. doi:10.1002/sim.2151PubMedGoogle ScholarCrossref
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Proschan  MA , Lan  KG , Wittes  JT . Statistical Monitoring of Clinical Trials: A Unified Approach. Springer Science & Business Media; 2006.
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Gordon Lan  KK , Simon  R , Halperin  M .  Stochastically curtailed tests in long-term clinical trials.   Sequential Anal. 1982;1(3):207-219. doi:10.1080/07474948208836014Google ScholarCrossref
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Andersen  PK .  Conditional power calculations as an aid in the decision whether to continue a clinical trial.   Control Clin Trials. 1987;8(1):67-74. doi:10.1016/0197-2456(87)90027-4PubMedGoogle ScholarCrossref
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Berry  DA .  Bayesian clinical trials.   Nat Rev Drug Discov. 2006;5(1):27-36. doi:10.1038/nrd1927PubMedGoogle ScholarCrossref
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Spiegelhalter  DJ , Freedman  LS , Blackburn  PR .  Monitoring clinical trials: conditional or predictive power?   Control Clin Trials. 1986;7(1):8-17. doi:10.1016/0197-2456(86)90003-6PubMedGoogle ScholarCrossref
9.
Saville  BR , Connor  JT , Ayers  GD , Alvarez  J .  The utility of bayesian predictive probabilities for interim monitoring of clinical trials.   Clin Trials. 2014;11(4):485-493. doi:10.1177/1740774514531352PubMedGoogle ScholarCrossref
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Dmitrienko  A , Wang  MD .  Bayesian predictive approach to interim monitoring in clinical trials.   Stat Med. 2006;25(13):2178-2195. doi:10.1002/sim.2204PubMedGoogle ScholarCrossref
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