2016

In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model parameters and the number of censored outcomes. In order to implement such a design in practice, an adaptive design that incorporates updated knowledge about the dose-response curve at interim analyses can be used [1]. Further, treatment allocation should involve randomization, which is essential to mitigate various experimental biases and perform valid statistical inference at the end of the trial. Here, we compare several randomization procedures and their impact on model estimation. Full Article: Implementing Optimal Designs