Bayesian semiparametric predictive modeling with applications in dose-response prediction

A framework is proposed for making quality predictions in situations for which only systematically inaccurate data are available. The predictions are based on the systematically inaccurate data, complete data from similar situations, and expert knowledge. The proposed predictive model is well suited to functional data and is computationally simple, fast, and stable. We focus primarily on a particular problem presenting itself in the pharmaceutical industry. Predicting both side effect and endpoint dose responses before the initiation of a clinical trial has enormous ethical and financial importance in the pharmaceutical industry. The proposed Bayesian semiparametric predictive model is used to predict unobserved clinical dose-response curves conditional on preclinical data, data from similar compounds, and prior knowledge. The model allows for nonlinear dose-response curves and the incorporation of relevant prior information. Posterior sampling is achieved through a simple and computationally efficient Gibbs sampler. The predictions from the model are drawn from the posterior distribution of the average dose-response curve for the candidate compound, allowing straightforward incorporation into a risk assessment model unlike the deterministic predictions often used currently. The model is used on actual data from the pharmaceutical industry, showing that the model is capable of predicting lack or presence of trend with appropriate uncertainty.