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Adaptive trials can reduce the length of your trial by up to 30%
Use adaptive designs like group sequential design to facilitate early interim decisions to ensure an efficient and ethical conclusion to your trial.
Avoid under or overpowered trials by using sample size re-estimation to adjust the sample size based on your blinded or unblinded interim results.
Investigate multiple treatments and dynamically drop or add trial arms using multi-arm, multi-stage (MAMS) designs
Stop trials early using spending function, Wang-Tsiatis, Haybittle-Peto and more
Adjust sample size dynamically using promising zone or internal pilot designs
Evaluate multiple treatments while dropping or adding study arms
Reject unpromising treatments faster using methods such as Simon’s Design
Combine Phase IIa/IIb evaluations for superior evidence for Phase II trials
Find the probability of success at each interim analysis
From fixed to flexible group sequential designs, nQuery helps you confidently reduce the risk and cost of clinical trials
Why nQuery is used for a wide range of adaptive designs
What is nQuery Adapt?
nQuery Adapt is the powerful adaptive 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 Adaptive module in nQuery here.
How do I take a trial?
You can take a free 14-day nQuery here.
How do I purchase?
nQuery Adapt is included in our Pro Tier.
In addition to the Adapt module, you also have access to all the popular frequentists and a Bayesian trial design functionality. With access to over 1000+ sample size scenarios.
You can purchase online here.
Have A Question?
You can get in contact with us here.
Are Adaptive designs more efficient?
Adaptive designs allow clinical trials to be more flexible by utilising results accumulated in the trial to change the trial’s course. Trials with an adaptive design are usually more efficient, informative and can be more ethical than trials that have a traditional fixed design because they often make better use of resources such as time and money, and may require fewer participants.
Adaptive designs can also be potentially less efficient than a fixed term trial or simple adaptive design if designed poorly. For example, sample size re-estimation designs lead to higher average sample sizes for minimal success rate increases or adaptive selection designs which include additional arms with minimal prior chance of succeeding.
Overall, adaptive designs when properly considered and well-planned have considerable scope for increasing trial efficiency but the additional flexibility could mean more opportunities for making poor design decisions.
Recommended:
An Introduction to Adaptive Clinical Trial Designs
What is a Group Sequential Design?
Group sequential designs are the most widely used type of adaptive trial in confirmatory Phase III clinical trials. Group sequential designs differ from a standard fixed term trial by allowing a trial to end early based on pre-specified interim analyses for efficacy or futility. Group sequential designs achieve this by using an error spending method that allows a set amount of the total Type I (efficacy) or Type II (futility) error at each interim analysis. The ability to end the trial can help reduce costs by creating an opportunity to get early approval for highly effective treatments and abandoning trials that have shown very poor results thus far.
The term 'futility' refers to the inability of a clinical trial to achieve its aims, such as, ending a clinical trial when the interim results suggest that it is highly unlikely to achieve statistical significance. This can save resources which can then be used in other more promising studies.
Recommended:
An Introduction to Adaptive Clinical Trial Designs
What is sample size re-estimation (SSR)?
Recommended Viewing:
Blinded Sample Size Re-Estimation in Clinical Trials
Conditional vs Predictive power?
Conditional power is the probability that the trial will reject the null hypothesis at a subsequent look given the current test statistic and the assumed “true” parameter values, which are usually assumed to equal their interim estimates or their initial planning values.
Predictive power (also known as Bayesian Predictive Power) is the conditional power averaged over the posterior distribution of the effect size. It is commonly used to quantify the probability of success of a clinical trial. It has been suggested as a superior alternative to conditional power as it treats the “true” estimates as uncertain rather than fixed.
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