Implementation of maximin efficient designs in dose‐finding studies |
| |
Authors: | Ellinor Fackle‐Fornius Frank Miller Hans Nyquist |
| |
Institution: | Department of Statistics, Stockholm University, Stockholm, Sweden |
| |
Abstract: | This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four‐step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose‐finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H‐algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose–response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design. © 2014 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd. |
| |
Keywords: | clinical study dose– response model extension of parameter set H‐algorithm maximin efficient design optimal design |
|
|