Batch Bayesian optimization design for optimizing a neurostimulator

Adam Kaplan, Thomas A. Murray

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, spinal epidural neurostimulation is being considered for rehabilitation of persons suffering from partial spinal-cord injury. The neurostimulator must be programmed by a neurosurgeon, yet little work has been done to develop rigorous methods for optimally programming the device. We propose an adaptive design to efficiently optimize programming of the neurostimulator based on specified interim evaluations of patient reported preferences. Preferences for the eligible device configurations are estimated after each interim analysis through a conditionally autoregressive model that assumes preference for one configuration is related to preferences for neighboring configurations. Using the adaptively updated preferences, a group of configurations is programmed into the device for the patient to evaluate during the next follow-up period. This selection is based on a balance of device exploration and preference maximization. We repeat this process until a specified stopping rule or the calibration end is reached. We show simulation studies to evaluate the overall quality of the adaptive calibration for various configuration selection strategies and the effects of stopping it early.

Original languageEnglish (US)
Pages (from-to)661-674
Number of pages14
JournalBiometrics
Volume77
Issue number2
DOIs
StatePublished - Jun 2021

Bibliographical note

Funding Information:
We thank the AE and two anonymous reviewers for constructive suggestions. The work of TM was partially supported by NIH/NCI award P30-CA0077598 and the E-STAND Award from the Minnesota Office of Higher Education Spinal Cord Injury and Traumatic Brain Injury Grant Program. TM would also like to acknowledge the support of Medtronic Inc. in the form of a Faculty?Fellowship.

Funding Information:
We thank the AE and two anonymous reviewers for constructive suggestions. The work of TM was partially supported by NIH/NCI award P30‐CA0077598 and the E‐STAND Award from the Minnesota Office of Higher Education Spinal Cord Injury and Traumatic Brain Injury Grant Program. TM would also like to acknowledge the support of Medtronic Inc. in the form of a Faculty Fellowship.

Publisher Copyright:
© 2020 The International Biometric Society

Keywords

  • adaptive design
  • medical device
  • n-of-1 trial
  • personalized medicine
  • spinal-cord injury

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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