The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
In this blog, we talk with Heiko Götte, Senior Expert Biostatistician at Merck about his upcoming presentation at Cytel’s East User Group Meeting on 14th and 15th November at Merck Darmstadt, in Germany. The topic Heiko will address is Decision Making in Development Programs with Targeted Therapies and he explains to us why this is a key topic for pharmaceutical companies today as they strive to improve their decision-making, and what delegates can expect to take away from the presentation.
Unlike statistics which has been around in some form for hundreds of years, pharmacometrics is, by comparison, a relatively new discipline and only entered the clinical development world in the last 30 years. Situated at the intersection of mathematical modeling, simulation, and big data, pharmacometrics leverages the best practices of translational research to generate clinical development strategy.
QPP (sometimes called QP2) remains at the heart of model based drug development. Short for Quantitative Pharmacology & Pharmacometrics, it refers to several types of quantitative modeling including meta-analysis, PK/PD , statistical modeling and the modeling of go-no-go decision rules.
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.
Our Client's Challenge:
Can knowledge of the relationship between biomarkers and clinical endpoints help us to optimize an early development program and improve the probability of selecting the right dose in Phase 3?
Our client approached us hoping to expedite dose-finding with biomarkers in Phase 1b, and to design an optimal Phase 2b clinical endpoint trial to maximize probability of correct Phase 3 dose selection.