Testing effect of a drug using multiple nested models for the dose–response. Issue 2 (6th February 2015) - Baayen, C.; Hougaard, P.; Pipper, C. B.
Summary During development of a drug, typically the choice of dose is based on a Phase II dose‐finding trial, where selected doses are included with placebo. Two common statistical dose‐finding methods to analyze such trials are separate comparisons of each dose to placebo (using a multiple comparison procedure) or a model‐based strategy (where a dose–response model is fitted to all data). The first approach works best when patients are concentrated on few doses, but cannot conclude on doses not tested. Model‐based methods allow for interpolation between doses, but the validity depends on the correctness of the assumed dose–response model. Bretz et al. (2005, Biometrics 61, 738–748) suggested a combined approach, which selects one or more suitable models from a set of candidate models using a multiple comparison procedure. The method initially requires a priori estimates of any non‐linear parameters of the candidate models, such that there is still a degree of model misspecification possible and one can only evaluate one or a few special cases of a general model. We propose an alternative multiple testing procedure, which evaluates a candidate set of plausible dose–response models against each other to select one final model. The method does not require any a priori parameter estimates and controls the Type I error rate of selecting a too complex model.