In group sequential designs and other similar designs, access to the interim data provides the opportunity to improve a study to better reflect the updated understanding of the study. One way a group sequential design would be to use the interim effect size estimate not only to decide to whether to stop a trial early but to increase the sample size if the interim effect size is promising. This optionality gives the trialist the chance to power for a more optimistic effect size, thus reducing up-front costs, while still being confident of being able to find for a smaller but clinically relevant effect size by increasing sample size if needed.
The most common way to define whether an interim effect size is promising is conditional 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 parameter values, which are usually assumed to equal their interim estimates. For “promising” trials where the conditional power falls between a lower bound, a typical value would be 50%, and the initial target power the sample size can be increased to make the conditional power equal the target study power.
nQuery Adapt also provides tables for unblinded sample size re-estimation. These tables allow nQuery Adapt users to extend their initial group sequential design by giving tools which allow users to conduct interim monitoring and conduct a flexible sample size re-estimate at a specified interim look. The tables in nQuery Adapt that aid you with Unblinded Sample Size Re-estimation are as follows:
- Unblinded Sample Size Re-estimation & Interim Monitoring for Two Means
- Unblinded Sample Size Re-estimation & Interim Monitoring for Two Proportions
Both these tables will be accessible by designing a group sequential study using the relevant group sequential designs and using the “Interim Monitoring & Sample Size Re-estimation” option from the group sequential “Looks” table. These tables will provide for two common approaches to unblinded sample size re-estimation: Chen-DeMets-Lan and Cui-Hung-Wang. There is also an option to ignore the sample size re-estimation and conduct interim monitoring for standard group sequential design.
The Chen-DeMets-Lan Method
The Chen-DeMets-Lan method allows a sample size increase while using the standard group sequential unweighted Wald statistics without appreciable error inflation, assuming an interim result has sufficiently "promising" conditional power. The primary advantages of the Chen-DeMets-Lan method are being able to use the standard group sequential test statistics and that each subject will be weighted equally to the equivalent group sequential design after a sample size increase. However, this design is restricted to the final interim analysis and Type I error control is expected but not guaranteed depending on the sample size re-estimation rules.
The Cui-Hung-Wang Method
The Cui-Hung-Wang method uses a weighted test statistic, using pre-set weights based on the initial sample size and the incremental interim test statistics, which strictly controls the type I error. However, this statistic will differ from that for a standard group sequential design after a sample size increase and since subjects are weighted on the initial sample size, those subjects in the post-sample size increase cohort will be weighted less than those before.
There is full control over the rules for the sample size re-estimation including sample size re-estimation look (for Cui-Hung-Wang), maximum sample size, whether to increase to the maximum sample size or the sample size to achieve the target conditional power and bounds for what a “promising” condition power is, among others.
Future nQuery Adapt updates will increase the number of study designs available, including for survival studies, and the number of options and flexibility for planning an unblinded sample size re-estimation.
