Description
Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery. Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods used in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies introductory and advanced statistical methods, providing a practical roadmap that will be invaluable for learners and experts alike.
Topics include a primer on version control and coding, pre-processing of imaging and electrophysiological data, hypothesis testing, generalized linear models, survival analysis, network analysis, time-series analysis, spectral analysis, spatial statistics, unsupervised and supervised learning, natural language processing, prospective trial design, pharmacokinetic and pharmacodynamic modeling, and randomized clinical trials.
Features:
- Provides a comprehensive introduction to statistical methods employed in epilepsy research
- Divided into four parts: Basic Processing Methods for Data Analysis; Statistical Models for Epilepsy Data Types; Machine Learning Methods; and Clinical Studies
- Covers methodological and practical aspects, as well as worked-out examples with R and Python code provided in the online supplement
- Includes contributions by experts in the field
- https://github.com/sharon-chiang/Statistics-Epilepsy-Book/
The handbook targets clinicians, graduate students, medical students, and researchers who seek to conduct quantitative epilepsy research. The topics covered extend broadly to quantitative research in other neurological specialties and provide a valuable reference for the field of neurology.
Table of Contents
1. Coding Basics. 2. Preprocessing Electrophysiological Data: EEG, iEEG and MEG Data. 3. Acquisition and Preprocessing of Neuroimaging MRI Data. 4. Hypothesis Testing and Correction for Multiple Testing. 5. Introduction to Linear, Generalized Linear and Mixed-Effects Models. 6. Survival Analysis. 7. Graph and Network Control Theoretic Frameworks. 8. Time-Series Analysis. 9. Spectral Analysis of Electrophysiological Data. 10. Spatial Modeling of Imaging and Electrophysiological Data. 11. Unsupervised Learning. 12. Supervised Learning. 13. Natural Language Processing. 14. Prospective Observational Study Design and Analysis. 15.Pharmacokinetic and Pharmacodynamic Modeling. 16. Randomized Clinical Trial Analysis.
