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E-grāmata: Statistical Methods in Epilepsy

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Statistical Methods in EpilepsyPalielināt
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"Epilepsy is a field of neurology centered on the evaluation and treatment of zero-inflated, seemingly random, and highly consequential events linked to high dimensional data over long- time scales and sparsely collected spatial data. Statistical methodsand modelling are central to the field of epilepsy and the scientific advancement of epilepsy management and treatment. This edited volume provides a clear and accessible overview of the use of statistical methods in epilepsy research. Topics include exploratory data analysis, hypothesis testing, power analysis, analysis of variance, regression, count data, survival analysis, directed/undirected networks, unsupervised learning, supervised learning, time-series analysis and circular statistics, and Bayesian analysis. Each chapter will feature worked examples of R, Matlab, and/or Python code integrated into the text. This book is intended to appeal to clinicians, applied statisticians, and data analysts interested in applying statistical methods to clinical data collected in epilepsy practice. The targeted audience consists of epilepsy clinicians and quantitative scientists involved in clinical epilepsy research. This audience will find this handbook valuable as a refence and introduction to specialized statistical topics"--

The handbook targets clinicians, graduate students, medical students, and researchers who seek to conduct quantitative epilepsy research.



Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods employed in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies statistical methods essential for epilepsy research, 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, spatial statistics, 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.

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.

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.

Sharon Chiang is a research fellow in the Department of Physiology and instructor in the Epilepsy Division in the Department of Neurology at the University of California, San Francisco, USA. Her research focuses on development of methods for state-space models in the estimation of seizure risk and neural mechanisms of memory consolidation in epilepsy.

Vikram R. Rao is Associate Professor of Clinical Neurology, Ernest Gallo Distinguished Professor, and Chief of the Epilepsy Division in the Department of Neurology at the University of California, San Francisco, USA. His clinical and research interests involve applications of neurostimulation devices for drug-resistant epilepsy, neuropsychiatric disorders, and seizure forecasting.

Marina Vannucci is Noah Harding Professor of Statistics at Rice University, Houston, TX, USA, and also holds an Adjunct Professor appointment at the MD Anderson Cancer Center, Houston, TX, USA. Her research is focused on the development of Bayesian statistical methodologies for application in genomics, neuroscience and neuroimaging.
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