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Optimum design and sequential treatment allocation in an experiment in deep brain stimulation with sets of treatment combinations
Author(s) -
Atkinson Anthony,
Pedrosa David
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7493
Subject(s) - randomization , set (abstract data type) , computer science , deep brain stimulation , design of experiments , sequential analysis , mathematical optimization , mathematics , statistics , medicine , surgery , randomized controlled trial , pathology , disease , parkinson's disease , programming language
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.