In the evolving landscape of clinical trial design, flexibility and efficiency have become essential for success. Sample size re-estimation (SSR) and population enrichment — both adaptive trial design methods — use interim data to make informed mid-trial adjustments. While they address different aspects — SSR focusing on how many patients to enroll and population enrichment focusing on which patients to include — both approaches aim to optimize trial outcomes, reduce unnecessary exposure, and make better use of limited resources.

This blog explores how these two methods work, their statistical underpinnings, and how they can be used to build more ethical, targeted, and cost-effective trials.

Sample size re-estimation

Sample size re-estimation is a type of clinical trial design adaptation in which the sample size can be reassessed at an interim look, based on accumulated data. Over the years, this method has grown in popularity for several reasons:

1. SSR designs address variability in an observed treatment effect when the treatment shows some promise, but the effect size is not as pronounced as originally expected.
2. SSR designs produce more ethical trials, as they limit the number of patients exposed to treatment until sufficient efficacy evidence is collected.
3. These designs provide flexibility in trial implementation in cases of hard-to-recruit patient populations or rare disease.
4. They allow for gatekeeping of investment for biotech companies who may undergo additional scrutiny to justify additional R&D spend.
5. They limit the pursuit of relatively small treatment effects that may not be clinically meaningful.

The CHW and CDL statistical methods for SSR

Following the seminal work on adaptive interim analysis by Bauer and Kohne (1994) and others, Cui, Hung, and Wang proposed a method that is today widely accepted in the field of biostatistics, combining statistics with pre-specified weights to preserve Type I error now known as CHW (1999). An alternative method proposed by Chen, DeMets, and Lan (2004) and known as CDL, provides an alternative to the use of the weighted statistic in a confirmatory two-arm, two-stage design where the sample size of the second stage is increased based on an unblinded analysis of the data at the first stage.

Both CHW and CDL are accepted by regulatory bodies such as the FDA in cases where such an adaptation is deemed appropriate. The CHW method applies a lower weight to the contributions of the second stage of the design relative to those of the first stage, and the CDL method permits the use of conventional statistics for testing the primary endpoint at the end of the study while still preserving Type I error.

Population enrichment

Population enrichment is a clinical trial design adaptation that allows for the use of data from an ongoing clinical trial to adjust the sample size of the entire study population, or a promising subpopulation based on a specific biomarker or other characteristics. At the outset, the overall trial population is enrolled in the study, regardless of biomarker status or other subgroup attribute. At the time of an interim analysis, a decision can be taken to either continue enrollment of the overall population, a subgroup of the population that is showing promise, or terminate the entire study for futility. Restricting enrollment to a specific subgroup enriches the data collected for this subpopulation.

There are several benefits for this adaptation, including:

• Optimizing resource allocation by enriching promising subpopulations while avoiding continued investment in less-successful subpopulations.
• It allows investigators the opportunity to examine a larger population while reducing the risk of trial failure or unnecessary drug exposure due to heterogeneity among the study’s subpopulations.
• At the same token, it increases the probability of success of a study, by increasing the sample size of promising subgroups.

How to model SSR and population enrichment

Both CDL and CHW methods for sample size re-estimation and population enrichment, are adaptations that can be modeled using Cytel’s East Horizon™ platform. Find out more by booking a product demonstration.

Final takeaways

Sample size re-estimation and population enrichment approaches are powerful adaptations in the biostatistician’s toolbox for advanced, cost-effective, and ethical clinical trial design. They empower sponsors to allocate R&D resources more appropriately towards promising treatments, while limiting exposure of patients to potentially ineffective or harmful treatments.

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