Group Sequential and Promising Zone Designs
Calculate boundaries, evaluate interim data & re-estimate sample size
Group Sequential and Promising Zone Designs
Adaptive in Nature
nQuery allows for modifications to the sample size based on interim results, enhancing flexibility and responsiveness to emerging data
Efficiency in Sample Size
Power for a smaller sample size based on realistic estimates but keep the flexibility to find lower clinically relevant effect sizes
Save Promising Trials
Save trials with promising interim results but a high probability of failure by increasing sample size using the Promising Zone design
nQuery’s Group Sequential and Unblinded Sample Size Re-estimation tools can maximize your chances of trial success
Find Stopping Rules
Find boundaries for early stopping for efficacy and futility
Evaluate Interim Data
Provide your interim data and find the optimal interim choice
Increase Sample Size
Save promising trials by increasing sample size based on conditional power
Compare Sequential Methods
Evaluate error spending, Wang-Tsiatis, Haybittle-Peto and more
Input Custom Boundaries
Input and convert between Z-value, p-value, Score and effect size boundaries
Understand Exit Probabilities
Evaluate the probability of early stopping under the null & alternative hypothesis
Designing Robust Group Sequential Trials
In this video, you will learn how you can maximise nQuery's Group Sequential Design features
Frequently Asked Questions
What is Group Sequential and Promising Zone Designs in nQuery?
nQuery Bayes is the powerful Bayesian module of the nQuery platform for clinical trial design.
nQuery has 1000+ validated statistical procedures covering Adaptive, Bayesian and classical clinical trial designs.
Browse the tables for the Bayesian module in nQuery here.
Group Sequential Design Theory
Randomization Lists tool allows for the easy generation of randomization lists that account both for randomization and any balancing covariates of interest.
The Randomization Lists tool will allow the user to generate randomization lists for trials with up to 25 treatments and centers (such as hospitals), and up to 2 additional stratification factors (such as age or gender) with up to 25 strata allowed for each. In addition, the user will be given algorithm-specific options (such as block size for block randomization) and the ability to add block or subject-level IDs for easier understanding and communication with stakeholders.
Recommended Resources
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