The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
At a recent Pfizer/ Cytel seminar on rare disease and oncology development, Cytel’s Lingyun Liu presented innovative work on a patient enrichment design. In this blog, we share some design and operational considerations. This approach can help mitigate against underpowering of a clinical trial where there is uncertainty and heterogeneity of treatment effect among subpopulations.
One consideration every sponsor of a biomarker-stratified confirmatory trial must take into account, is whether to evaluate the biomarker subpopulation (S) against the rest of the population (S') or against the full population (F).
Mathematically, one would think this makes very little difference as F is partitioned into S and S'. If the null hypothesis is rejected for both S and S' then clearly it is rejected for F too. Similarly, if it is rejected for S and not for S' then the therapy is effective for the biomarker subpopulation, and ineffective for the rest of the population.
As it turns out, whether or not a given biomarker is indeed a predictive biomarker should affect the choice of statistical methodology in time-to-event trials.
A key stage of exploratory drug development is implementing a proof-of-concept study to demonstrate the safety of a drug. Given the importance of accurate dose-finding for Phase 3 success, methodological improvements to proof-of-concept studies in Phase 2 can translate into greater likelihood of getting a drug to market.
When designing clinical trials, many trial designers are advised to keep the trial simple. Prima facie, the keep it simple principle seems like sound advice. There are various logistical uncertainties that arise when implementing a clinical trial, and the more simple a trial – so conventional wisdom says – the easier it is to respond to these uncertainties.
According to Zoran Antonijevic, a Senior Director at Cytel Consulting, there is reason to doubt such conventional wisdom. After all, flexibility is hardly a virtue of a traditional trial design. Simple designs may seem to make it easier to monitor data and report results. However, a flexible design can better address remaining uncertainties in product development. These uncertainties are related to treatment effect, dose selection, or a sub-population that would experience the best benefit/risk from the treatment.
Complexities with identifying suitable test populations in oncology studies contribute significantly to the 60% attrition rate in Phase III trials. Cyrus Mehta, (President of Cytel) has recently authored a paper on ‘Biomarker Driven Population Enrichment for Adaptive Oncology Trials,’ (forthcoming in Statistics in Medicine) which provides an innovative method for using two-stage adaptive designs for population enrichment.
Mehta, et al., are sensitive to the dilemma faced by Phase III trial designers choosing between open and restricted enrollment. Open enrollment allows for a large number of patients, and ensures that all patients who may benefit from a therapy have an opportunity to be involved. By contrast, limiting enrollment is a superior practice for revealing the efficacy of a trial for a targeted population. The proposed method allows for biomarker driven enrichment at interim analysis, meaning that only those subgroups that appear to benefit from therapy need to progress to the second stage of the trial.
Cytel Consulting's Zoran Antonijevic
The key focus of precision medicine is identification of patients who would most benefit from a treatment. Proper enrichment of patient population greatly improves the probability of regulatory approval as well as product differentiation through improved efficacy and safety. Greater product differentiation leads to greater market access, as reimbursement is now a key driver to commercial success.