Statistical Methods in Epilepsy
Chapman & Hall/CRC (Verlag)
978-1-032-18435-7 (ISBN)
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.
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.
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.
| Erscheinungsdatum | 27.03.2024 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Interdisciplinary Statistics |
| Zusatzinfo | 27 Tables, black and white; 95 Line drawings, black and white; 95 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 930 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Medizin / Pharmazie ► Medizinische Fachgebiete ► Neurologie | |
| Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
| ISBN-10 | 1-032-18435-3 / 1032184353 |
| ISBN-13 | 978-1-032-18435-7 / 9781032184357 |
| Zustand | Neuware |
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