A SIMULATION STUDY COMPARING DESIGNS FOR DOSE RANGING

Only with knowledge of the (prior) distribution of dose-response parameters in a population, can one determine both the initial dose of a drug for chronic administration to an individual (such as the dose producing a fixed degree of response in a fixed proportion of the population) and an appropriate subsequent (adjusted) dose (such as the dose yielding a desirable response according to the posterior parameter distribution, given an observed response to an initial dose). The currently FDA-sanctioned design for a dose-ranging study, the parallel-dose design, assigns just one of several doses to each patient. It does not provide good information on the distribution of individual dose-response parameters. A cross-over design assigns several dose levels to each patient. It therefore can provide better information, but does not resemble clinical practice. Consequently, study participants must be restricted to patients who can tolerate such non-therapeutic drug exposure, posing problems in extrapolation of study results to other types of patients. A titration or dose-escalation design begins all patients on placebo and, except for those patients assigned to a placebo-only group, escalates the dose for a patient at present intervals only when clinical response at lower doses is inadequate. It both exposes patients to several dose levels and resembles good clinical practice, allowing study of a representative patient sample. We report here the simulation results of parameter estimation for the three designs when the data arise from complex and realistic dose-response models and/or with certain complications in study execution. The dose-escalation design clearly performs better overall than the parallel-dose design for the models considered here, and generally, just a little worse than the cross-over design. These results support the conclusion that for dose ranging, depending on the demands of the clinical situation, one should use either the cross-over or the dose-escalation design.