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Start Over Descriptor "predictive probabilities" Topic sample size
4 results on '"predictive probabilities"'

1. Not too big, not too small: a goldilocks approach to sample size selection.

Author

Broglio KR, Connor JT, and Berry SM

Subjects
Algorithms, Bayes Theorem, Binomial Distribution, Breast Neoplasms mortality, Breast Neoplasms pathology, Endpoint Determination statistics & numerical data, Female, Humans, Lymph Nodes pathology, Predictive Value of Tests, Proportional Hazards Models, Sentinel Lymph Node Biopsy, Survival Analysis, Clinical Trials, Phase III as Topic statistics & numerical data, Models, Statistical, Randomized Controlled Trials as Topic statistics & numerical data, Sample Size
Abstract

We present a Bayesian adaptive design for a confirmatory trial to select a trial's sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, "Is the sample size too big, too small, or just right?" We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints.

Published
2014
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2. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial: an adaptive trial design case study

Author

Kristine Broglio, William J. Meurer, Karen C. Johnston, Valerie Durkalski, and Jason T. Connor

Subjects
Research design, medicine.medical_specialty, Predictive probabilities, MEDLINE, Medicine (miscellaneous), Bioinformatics, Brain Ischemia, law.invention, Neurologic emergencies, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, law, Clinical endpoint, Humans, Insulin, Medicine, Medical physics, Pharmacology (medical), 030212 general & internal medicine, Adaptive clinical trial, business.industry, Research, Bayes Theorem, Bayesian adaptive design, 3. Good health, Stroke, Clinical trial, Research Design, Sample size determination, Hyperglycemia, Sample Size, Goldilocks principle, Sample size estimation, business, Group sequential, 030217 neurology & neurosurgery
Abstract

The ‘Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT)’ project is a collaborative effort supported by the National Institutes of Health (NIH) and United States Food & Drug Administration (FDA) to explore how adaptive clinical trial design might improve the evaluation of drugs and medical devices. ADAPT-IT uses the National Institute of Neurologic Disorders & Stroke-supported Neurological Emergencies Treatment Trials (NETT) network as a ‘laboratory’ in which to study the development of adaptive clinical trial designs in the confirmatory setting. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial was selected for funding by the NIH-NINDS at the start of ADAPT-IT and is currently an ongoing phase III trial of tight glucose control in hyperglycemic acute ischemic stroke patients. Within ADAPT-IT, a Bayesian adaptive Goldilocks trial design alternative was developed. The SHINE design includes response adaptive randomization, a sample size re-estimation, and monitoring for early efficacy and futility according to a group sequential design. The Goldilocks design includes more frequent monitoring for predicted success or futility and a longitudinal model of the primary endpoint. Both trial designs were simulated and compared in terms of their mean sample size and power across a range of treatment effects and success rates for the control group. As simulated, the SHINE design tends to have slightly higher power and the Goldilocks design has a lower mean sample size. Both designs were tuned to have approximately 80% power to detect a difference of 25% versus 32% between control and treatment, respectively. In this scenario, mean sample sizes are 1,114 and 979 for the SHINE and Goldilocks designs, respectively. Two designs were brought forward, and both were evaluated, revised, and improved based on the input of all parties involved in the ADAPT-IT process. However, the SHINE investigators were tasked with choosing only a single design to implement and ultimately elected not to implement the Goldilocks design. The Goldilocks design will be retrospectively executed upon completion of SHINE to later compare the designs based on their use of patient resources, time, and conclusions in a real world setting. ClinicalTrials.gov NCT01369069 June 2011.

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