Oncology research has seen many changes and advances in recent decades, from new therapies in combination with backbone chemotherapy to novel treatments targeting malignancies, and compounds targeting specific disease biomarkers at the genetic mutation level. The latter approach has called to question large, relatively long clinical studies assessing the safety and efficacy of treatments against a large population defined at the tumor level. Rather, research at the subpopulation or biomarker level has garnered much more interest as targeted treatments are being developed.  

This focus on subpopulations and biomarkers is changing how researchers approach clinical trials in oncology and helps resolve several issues with larger clinical trials. For example, treatment effects may be diluted in a heterogeneous population, possibly resulting in an underpowered study. Furthermore, a large trial in a heterogeneous population may place patients for whom the drug is ineffective at risk of serious adverse events. On the other hand, restricting enrollment to a target subgroup without sufficient evidence may deny a large segment of the patient population access to a potentially beneficial treatment. This blog post will briefly introduce two statistical approaches addressing the rise of more specific study populations: predefined subpopulation statistical analysis in the context of a larger trial population and population enrichment of the more promising subgroup within an ongoing study. 

Subpopulation Analysis 

Subpopulation testing and analysis is a phase III clinical trial design strategy in which a subset of the study population is selected based on patient characteristics that may be more likely to respond to the treatment under investigation. Identifying and analyzing specific subpopulations allows the researcher to explore whether a treatment leads to different effects in a pre-designated subpopulation. A subpopulation can be defined by any stratification characteristic such as gender or geography, and in oncology clinical trials, specific biomarkers identified within a study population. 

This type of approach to clinical research has several significant benefits in Oncology studies: 

• A large trial in a heterogeneous population may place patients for whom the drug is ineffective at risk of serious adverse events. 

• In a heterogenous population, the treatment effect may be diluted, possibly resulting in an underpowered study. 

• Restricting enrollment to the targeted subgroup without sufficient statistical evidence of lack of efficacy in the non‐targeted subgroup may eliminate beneficial treatment options for patients. 

• Subpopulation analysis allows for treatment recommendations based on individual characteristics. 

As with any novel adaptive design approach, subpopulation analysis requires several considerations at the design stage. These considerations include the specific definition of the subpopulations for analysis in the study, the appropriate timing for an interim analysis, the methods used for hypothesis testing and type-1 error preservation, and the sequence of hypothesis testing of the different subpopulations and/or the full study population.  

With these considerations in mind, rigorous planning and testing in the design stage of such a clinical trial is critical. Cytel’s East Horizon adaptive clinical trial design software offers a unique solution for the planning and testing of a clinical trial design that includes subpopulation analysis. In Cytel’s solution, hypothesis testing for the full and subpopulations can be performed using graphical multiple comparison procedures (gMPC) with a weighted Bonferroni procedure employed for closed testing. This method of hypothesis testing uses directed, weighted graphs where each node corresponds to a single hypothesis. A transition matrix is used as a complement to specify the weights and generate an intuitive diagram. Finally, a simple algorithm sequentially tests the individual hypotheses using the specified weights and hierarchies. 

Population Enrichment 

Population Enrichment is an adaptive clinical trial approach that includes the prospective use of any patient characteristic to obtain a study population in which detection is more likely than in the unselected population. There are two types of population enrichment: Prognostic Enrichment, in which a high-risk patient population is identified based on a biomarker, and Predictive Enrichment, in which the researchers identify a patient group more likely to respond to treatment. Some industry trends that have contributed to the popularization of this adaptive design method include the soaring costs of clinical trial execution, a move away from a “one-size-fits-all” approach to clinical development, and the rising interest in individualized medicine. This adaptive design approach has several benefits, including the identification of highly responsive patient populations, the efficient detection of a treatment effect in a smaller sample size, and the ability to identify beneficial treatments for a subgroup of patients that may have failed with a broader population in a more traditional study design.  

Population enrichment can be seen as an extension of the sample size re-estimation (SSR) methodology, which we discussed in more depth in a previous blog post. 

In the enrichment adaptive approach, a pre-specified number of subjects comprising the entire population, designated as cohort 1, is tested in an interim analysis, and a data monitoring committee reviews the results to assess efficacy or futility against predetermined thresholds. Suppose the analysis shows promising results for only a specific subpopulation of interest in the study, this population is “enriched” with additional patient enrollment in the remaining number of subjects of the study, designated as cohort 2, to enhance data collection for only this subgroup of interest and increase the overall probability of success of the study. As with any adaptive approach, this method has specific considerations, including closed testing with a p-value combination, the preservation of type-1 errors, and additional special considerations requiring attention in event-driven trials like most oncology ones.  

Final Takeaways 

Both subpopulation analysis and population enrichment are adaptive approaches to modern trial designs in oncology that offer great hope for researchers and patients alike. As the focus on specific patient populations narrows, these adaptive design types are gaining industry traction. Software-guided clinical trial design and simulation using tools such as East Horizon ensure adaptive elements are incorporated thoughtfully and are rigorously tested prior to trial launch. 

Learn more about these approaches in our upcoming webinar ‘’Oncology Clinical Trials: Design Trends in Biomarker-Driven Research’’ with Boaz Adler and Valeria Mazzanti.

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