The Type II Error Probability of a Group Sequential Test of Efficacy and Futility, and Considerations for Power and Sample Size

Dobbins, Thomas W.

doi:10.1080/10543406.2011.617229

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The SHINE trial was executed using a group sequential trial design with 2-sided efficacy and futility stopping boundaries along with response-adaptive randomization. The stopping boundaries were defined using the Gamma family spending functions and closely resembled an O’Brien-Fleming boundary . Futility boundaries were considered nonbinding; therefore, if a futility boundary was crossed, the trial could be continued without penalty.…”

Section: Methodsmentioning

confidence: 99%

“…The stopping boundaries were defined using the Gamma family spending functions and closely resembled an O'Brien-Fleming boundary. 17 Futility boundaries were considered nonbinding; therefore, if a futility boundary was crossed, the trial could be continued without penalty. Interim analyses were planned when 500, 700, 900, and 1100 consecutively randomized patients had reached the 90-day follow-up period.…”

Section: Shine Trialmentioning

confidence: 99%

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Comparison of Bayesian vs Frequentist Adaptive Trial Design in the Stroke Hyperglycemia Insulin Network Effort Trial

et al. 2022

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IMPORTANCE Bayesian adaptive trial design has the potential to create more efficient clinical trials.However, a barrier to the uptake of bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared with a frequentist design. OBJECTIVETo compare the performance of a bayesian and a frequentist adaptive clinical trial design. DESIGN, SETTING, AND PARTICIPANTSThis prospective cohort study compared 2 trial designs for a completed multicenter acute stroke trial conducted within a National Institutes of Health neurologic emergencies clinical trials network, with individual patient-level data, including the timing and order of enrollments and outcome ascertainment, from 1151 patients with acute stroke and hyperglycemia randomized to receive intensive or standard insulin therapy. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses at 90 days after randomization for 500, 700, 900, and 1100 patients. The bayesian alternative used predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients and subsequent interims after every 100 randomizations. MAIN OUTCOMES AND MEASURESThe main outcome was the sample size at end of study, which was defined as the sample size at which each of the studies stopped accrual of patients.RESULTS Data were collected from 1151 patients. As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the bayesian alternative crossed the futility boundary approximately 3 months earlier after 800 participants were randomized. CONCLUSIONS AND RELEVANCEBoth trial designs stopped for futility before reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the 2 designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how these analyses were implemented between the 2 trials resulted in the differences in early stopping.

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Section: Methodsmentioning

confidence: 99%

Section: Shine Trialmentioning

confidence: 99%

Comparison of Bayesian vs Frequentist Adaptive Trial Design in the Stroke Hyperglycemia Insulin Network Effort Trial

et al. 2022

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“…The stopping boundaries were defined using the gamma family spending functions and closely resembled an O’Brien-Fleming boundary. 16 Futility boundaries were considered non-binding, meaning that if a futility boundary was crossed, the trial could be continued without inflation of the Type 1 error rate. Interim analyses were planned when 500, 700, 900, and 1,100 consecutively randomized patients had reached the 90-day follow up period.…”

Section: Methodsmentioning

confidence: 99%

“…The copyright holder for this preprint this version posted June 7, 2021. ; https://doi.org/10.1101/2021.06.02.21257838 doi: medRxiv preprint Favorable for the primary efficacy outcome is defined as modified Rankin Scale (mRS) score of 0 in patients with mild stroke (baseline NIHSS 3-7), mRS 0-1 in patients with moderate stroke (baseline NIHSS 8-14), and mRS 0-2 in patients with severe stroke (baseline NIHSS[15][16][17][18][19][20][21][22]. The adjusted risk difference for the primary outcome was adjusted for baseline stroke severity (NIHSS 3-7 (mild); 8-14 (moderate); 15-22 (severe)) and thrombolysis use (yes/no).NIHSS -National Institutes of Health Stroke Scale.…”

mentioning

confidence: 99%

Prospective comparison of Bayesian and frequentist adaptive clinical trials : The SHADOW - SHINE project

Broglio

Meurer

Durkalski

et al. 2021

Preprint

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Importance: Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, one of the barriers to the uptake of Bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared to a frequentist design. Objective: Compare the performance of a Bayesian and a frequentist adaptive clinical trial design. Design: Prospective observational study comparing two trial designs using individual patient level data from a completed stroke trial, including the timing and order of enrollments and outcome ascertainment. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses when 90-days post-randomization was met for 500, 700, 900, and 1,100 patients. The Bayesian alternative utilized predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients, and subsequent interims after every 100 randomizations. Setting: Multi-center, acute stroke study conducted within a National Institutes of Health neurological emergencies clinical trials network. Participants: Patient level data from 1,151 patients randomized in a clinical trial comparing intensive insulin therapy to standard in acute stroke patients with hyperglycemia. Main Outcome(s) and Measure(s): Sample size at end of study. This was defined as the sample size at which each of the studies stopped accrual of patients. Results: As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the Bayesian alternative crossed the futility boundary about 3 months earlier after 800 participants were randomized. Conclusions and Relevance: Both trial designs stopped for futility prior to reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the two designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how this was implemented between the two trials resulted in the differences in early stopping.

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An ROC Approach to Evaluate Interim Go/No-Go Decision-Making Quality with Application to Futility Stopping in the Clinical Trial Designs

Wang

Zhang

Yang³

2016

New Developments in Statistical Modeling, Inference and Application

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The Type II Error Probability of a Group Sequential Test of Efficacy and Futility, and Considerations for Power and Sample Size

Cited by 5 publications

References 6 publications

Comparison of Bayesian vs Frequentist Adaptive Trial Design in the Stroke Hyperglycemia Insulin Network Effort Trial

Comparison of Bayesian vs Frequentist Adaptive Trial Design in the Stroke Hyperglycemia Insulin Network Effort Trial

Prospective comparison of Bayesian and frequentist adaptive clinical trials : The SHADOW - SHINE project

An ROC Approach to Evaluate Interim Go/No-Go Decision-Making Quality with Application to Futility Stopping in the Clinical Trial Designs

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