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Plus why regulatory agencies are encouraging them
Adaptive trials enable continual modification to the trial design based on interim data. This means that with adaptive trials, you have the opportunity to make changes to your trial, while it is still ongoing. Which in turn can allow you to explore options and treatments that you would otherwise be unable to which can lead to improvements to your trial, based on data as it becomes available.
Interim sample size reassessment ensures sufficient power, more patients receive the superior treatment or transition directly from one trial phase to another. Combined this reduces the
Statistical analysis plans for adaptive clinical trials should cover interim analyses and Sample Size Re-estimation plans.
nQuery Adapt provides Biostatisticians with a range of tables that spans various adaptive disciplines for sample size calculation.
This Adaptive design module in nQuery contains 15 tables and spans 4 adaptive disciplines. Click on each below to learn more about them and how they can help you reduce the risk and cost of your trials.
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 at pre-specified interim analyses for efficacy or futility. Group sequential designs achieve this by using an error spending method which allows a set amount of the total Type I (efficacy) or Type II (futility) error at each interim analysis. This design thus allows the trialist the flexibility to end those trials early which would otherwise have needed another large cohort of subjects to be analysed unnecessarily.
The tables in nQuery Adapt that aid you with Group Sequential Designs are as follows:
These tables extend nQuery’s capabilities into one sample designs and also allow survival models with greater flexibility regarding follow-up time and accrual.
In group sequential designs and other adaptive designs, access to the interim data gives the ability to answer the important question of how likely a trial is to succeed based on the information accrued so far. The two most commonly cited statistics to evaluate this is conditional power and 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 parameter values, which are usually assumed to equal their interim estimates. Predictive power (also known as Bayesian Predictive Power) is the conditional power averaged over the posterior distribution of the effect size. Both of these give an indication of how promising a study is based on the interim data and are important both as ad-hoc measures of futility testing and defining the range of values useful for unblinded sample size re-estimation.
In nQuery Adapt, users can analyse and investigate different scenarios and assumptions for how likely a trial is to succeed based on the interim data. The tables in nQuery Adapt that aid you with Conditional Power and Predictive Power are as follows:
These tables will allow conditional and predictive power to be calculated simultaneously by assuming a diffuse prior for the predictive power calculation. Future updates will extend the number of design scenarios covered and additional flexibility in priors for predictive power.
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:
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.
Sample size determination always requires a level of uncertainty over the assumptions made to find the appropriate sample size. Many of these assumed values are for nuisance parameters which are not directly related to the effect size.
As such it would be useful to have a better estimate for these values than relying on external sources or the cost of a separate pilot study but without the additional regulatory and logistical costs of using unblinded interim data.
Blinded sample size re-estimation allows the estimation of improved estimates for these nuisance parameters without unblinding the study.
In the current nQuery Adapt, the internal pilot method assigns an initial cohort of subjects as the “pilot study” and then calculates an updated value for a nuisance parameter of interest. This updated nuisance parameter value is then used to increase the study sample size if required, with the final analysis conducted with standard fixed term analyses with the internal pilot data included.
The Blinded Sample Size Re-estimation tables in nQuery Adapt allow users to seamlessly conduct an internal pilot study for common two means and two proportions design scenarios. The tables in nQuery Adapt that aid you with Blinded Sample Size Re-estimation are as follows:
nQuery Adapt provides full flexibility over the size of the internal pilot study, whether sample size decreases are allowable in addition to increase and tools to derive the best-blinded estimate from the internal pilot.
Blinded sample size re-estimation for the two sample t-test updates the sample size based on a blinded estimate of the common within-group standard deviation. Three methods are available to estimate the within-group standard deviation from the internal pilot data: pilot standard deviation, bias-adjusted pilot standard deviation, upper confidence limit for pilot standard deviation.
Blinded sample size re-estimation for the two
The FDA recently published new draft guidance on adaptive trials and are actively encouraging sponsors to use Adaptive trials.
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Duration: 60 minutes