A bayesian dose-finding design adapting to efficacy and tolerability response

We propose a new adaptive Bayesian design, explicitly modeling the trade-off between efficacy and tolerability in dose-finding studies. This design incorporates a continuous efficacy variable and a dichotomous tolerability variable. This adaptive design was developed in the context of a drug under development for treatment of major depression, but is easily extended to any setting with a continuous efficacy and a dichotomous tolerability or safety variable. The goal is to identify a target dose that was most efficacious while still being safe. Via simulations under various scenarios we show that our design performs extremely efficiently. Our design incorporates stopping rules, adaptive allocation, and dose-response estimation (for both efficacy and tolerability), among other features. We present various metrics from our simulation study, and conclude that this is an extremely efficient way of characterizing the risk-benefit profile of a drug during clinical development.