Optimum design and sequential treatment allocation in an experiment in deep brain stimulation with sets of treatment combinations

Stat Med. 2017 Dec 30;36(30):4804-4815. doi: 10.1002/sim.7493. Epub 2017 Sep 28.

Abstract

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.

Keywords: Burman loss; extended equivalence theorem; prognostic factors; randomization; sequential design; treatment balance.

MeSH terms

  • Algorithms
  • Biostatistics
  • Computer Simulation
  • Deep Brain Stimulation / methods*
  • Deep Brain Stimulation / statistics & numerical data
  • Essential Tremor / therapy
  • Humans
  • Models, Statistical
  • Prognosis
  • Randomized Controlled Trials as Topic / statistics & numerical data