Curve fitting with confidence for preclinical dose-response data

Abstract

Abstract. In the preclinical stage of pharmaceutical drug development, when investigating the medicinal properties of a new compound, there are two important questions to address. The first question is simply whether the compound has a significant beneficial effect compared to vehicle (placebo) or reference treatments. The second question concerns the more nuanced dose–response relationship of the compound of interest. One of the aims of this thesis is to design an experiment appropriate for addressing both of these questions simultaneously. Another goal is to make this design optimal, meaning that dose-levels and sample sizes are arranged in a manner which maximises the amount of information gained from the experiment. We implement a method for assessing efficacy (the first question) in a modelling environment by basing inference on the confidence band of a regression curve. The verdicts of this method are compared to those of one-way anova coupled with the multiple comparison procedure Dunnett’s test. When applied to our empirical data sets, the two methods are in perfect agreement regarding which dose-levels have an effect at the 5% significance level. Through simulation, we find that our modelling approach tends to be more conservative than Dunnett’s test in trials with few dose-levels, and vice versa in trials with many dose-levels. Furthermore, we investigate the effect of optimally designing the simulated trials, and also the consequences of misspecifying the underlying dose–response model during regression, in order to assess the robustness of the implemented method.