Optimum design and sequential treatment allocation in an experiment in deep brain stimulation with sets of treatment combinations
| Autor: | Anthony B. Atkinson, David J. Pedrosa |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Mathematical optimization Deep brain stimulation Randomization Epidemiology medicine.medical_treatment 01 natural sciences Sequential treatment 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Deep brain stimulation electrode Sequential analysis medicine 0101 mathematics Set (psychology) 030217 neurology & neurosurgery Mathematics |
| Zdroj: | Statistics in Medicine. 36:4804-4815 |
| ISSN: | 0277-6715 |
| DOI: | 10.1002/sim.7493 |
| Popis: | In an experiment including patients who underwent surgery for deep brain stimulation electrode placement, each patient responds to a set of 9 treatment combinations. There are 16 such sets, and the design problem is to choose which sets should be administered and in what proportions. Extensions to the methods of nonsequential optimum experimental design lead to identification of an unequally weighted optimum design involving 4 sets of treatment combinations. In the actual experiment, patients arrive sequentially and present with sets of prognostic factors. The idea of loss due to Burman is extended and used to assess designs with varying randomization structures. It is found that a simple sequential design using only 2 sets of treatments has surprisingly good properties for trials with the proposed number of patients. |
| Databáze: | OpenAIRE |
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