Biomarker informed adaptive clinical trial designs
In adaptive design clinical trials, an endpoint at the final analysis that takes a long time to observe is not feasible to be used for making decisions at the interim analysis. For example, overall survival (OS) in oncology trials usually cannot be used to make interim decisions. However, biomarkers correlated to the final clinical endpoint can be used. Hence, considerable interest has been drawn towards the biomarker informed adaptive clinical trial designs. Shun et al. (2008) proposed a "biomarker informed two-stage winner design" with 2 active treatment arms and a control arm, and proposed a normal approximation method to preserve type I error. However, their method cannot be extended to designs with more than 2 active treatment arms. In this dissertation, we propose a novel statistical approach for biomarker informed two-stage winner design that can accommodate multiple active arms and control type I error. We further propose another biomarker informed adaptive design called "biomarker informed add-arm design for unimodal response". This design utilizes existing knowledge about the shape of dose-response relationship to optimize the procedure of selecting best candidate treatment for a larger trial. The key element of the proposed design is that, some inferior treatments do not need to be explored and the design is shown to be more efficient than biomarker informed two-stage winner design mathematically. Another important component in the study of biomarker informed adaptive designs is to model the relationship between the two endpoints. The conventional approach uses a one-level correlation model, which might be inappropriate if there is no solid historical knowledge of the two endpoints. A two-level correlation model is developed in this dissertation. In the new model a new variable that describes the mean level correlation is developed, so that the uncertainty of the historical knowledge could be more accurately reflected. We use this new model to study the "biomarker informed two-stage winner design" and the "biomarker informed add-arm design for unimodal response". We show the new proposed model performs better than conventional model via simulations. The concordance of inference based on biomarker and primary endpoint is further studied in a real case.