Adaptive dose-finding designs to identify multiple doses that achieve multiple response targets

Adrian Mander () and Simon Bond
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Simon Bond: Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation

United Kingdom Stata Users' Group Meetings 2013 from Stata Users Group

Abstract: Within drug development, it is crucial to find the right dose that is going to be safe and efficacious; this is often done within early phase II clinical trials. The aim of the dose-finding trial is to understand the relationship between the dose of drug and the potential effect of the drug. Increasingly, adaptive designs are being used in this area because they allow greater flexibility for dose exploration in comparison with traditional fixed-dose designs. An adaptive dose-finding design usually assumes a true nonlinear dose–response model and select doses that either maximize the determinant of information matrix of the design (D-optimality) or minimize the variance of the predicted dose that gives a targeted response. Our design extends the predicted dose methodology, in a limited number of patients (40), to finding two targeted doses: a minimally effective dose and a therapeutic dose. In our trial, doses are given intravenously, so theoretically, doses are continuous and the response is assumed to be a normally distributed continuous outcome. Our design has an initial learning phase where pairs of patients are assigned to five preassigned doses. The next phase is fully sequential with an interim analysis after each patient to determine the choice of dose based on the optimality criterion to minimize the determinant of the covariance of the estimated target doses. The dose–choice algorithm assumes that a specific parametric dose–response model is the true relationship, and so a variety of models are considered at the interim, and human judgment involved in the overall decision. I will introduce a Mata command that uses the optimize function to find the estimated parameters of the model and to subsequently find the optimal design. Simulated results show that assuming a model with a small number of parameters (=3) leads to a choice of doses that are not near to the target doses and overrely on interpolation under the modeling assumptions. Fitting models with greater flexibility with more parameters (=4) results in a choice of doses near to the two target doses. Overall, the design is efficient and seamlessly combines the initial learning and subsequent confirmatory stages.

Date: 2013-09-16
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