Patient-specific dose finding based on bivariate outcomes and covariates

Abstract

A Bayesian sequential dose-finding procedure based on bivariate (efficacy, toxicity) outcomes that accounts for patient covariates and dose-covariate interactions is presented. Historical data are used to obtain an informative prior on covariate main effects, with uninformative priors assumed for all dose effect parameters. Elicited limits on the probabilities of efficacy and toxicity for each of a representative set of covariate vectors are used to construct bounding functions that determine the acceptability of each dose for each patient. Elicited outcome probability pairs that are equally desirable for a reference patient are used to define two different posterior criteria, either of which may be used to select an optimal covariate-specific dose for each patient. Because the dose selection criteria are covariate specific, different patients may receive different doses at the same point in the trial, and the set of eligible patients may change adaptively during the trial. The method is illustrated by a dose-finding trial in acute leukemia, including a simulation study.