Download and explore the data featured in this webinar:
- Blinded SSR for Two Sample t-test for Inequality using Internal Pilot.nqt
- Blinded SSR for Two Sample X2 Test for Inequality using Internal Pilot.nqt
- Conditional Power for Two Means.nqt
- Group Sequential Test of Two Means.nqt
- Interim Monitoring and Unblinded Sample Size Re-estimation for Two Means.nqt
- Interim Monitoring and Unblinded Sample Size Re-estimation for Two Means.nqt
Guide to Sample Size Re-Estimation in Clinical Trials
Approaches for blinded and unblinded sample size re-estimation
We have explored common challenges faced when designing and conducting clinical trials with sample size re-estimation (SSR). Common SSR methods are explored along with their pros and cons. We have also examined some real-world examples of these methods, while taking a look at what advantages their use can bring.
You will learn about:
- Unblinded Sample Size Re-estimation for Means Data
- Cui-Hung-Wang SSR
- Chen-DeMets-Lan SSR
- Blinded Sample Size Re-estimation
Guide to Sample Size Re-Estimation in Clinical Trials
Sample Size Re-Estimation (SSR) is a type of adaptive trial design in which the total sample size can be re-estimated at an interim point in the trial. Its use has become increasingly common and important in the clinical trial landscape. Due to the complex nature of these adaptive trials, some unique challenges are faced.
Sample Size Re-Estimation can largely be split into two areas, blinded sample size re-estimation (BSSR) and unblinded sample size re-estimation (USSR). These aim to target different aspects of the uncertainty around the initial sample size determination of the trial.
Blinded sample size re-estimation is very useful in tackling the possible misspecification of any nuisance parameters, for example the variance. Whereas unblinded sample size re-estimation (USSR) will instead aim to focus on providing an unblinded estimate for the effect size and so aims to address any uncertainty regarding the magnitude of the initial effect size estimate used in the sample size determination.
In this tutorial, we have examined both of these areas and discussed some of the popular methods within each area. Utilising some real-world sample size examples to illustrate the challenges and solutions these methods provide.
nQuery helps make your clinical trials faster, less costly and more successful with tools for sample size calculation - refining Frequentist, Bayesian & Adaptive designs.
Start your unlimited 14-day trial to help design efficient, informative and ethical adaptive trials.
