The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.
There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability.
The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling.
Updates to this new and expanded edition include:
• A discussion of Big Data and its implications for the design of prediction models
• Machine learning issues
• More simulations with missing 'y' values
• Extended discussion on between-cohort heterogeneity
• Description of ShinyApp
• Updated LASSO illustration
• New case studies
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Ewout Steyerberg worked for 25 years at Erasmus Medical Center in Rotterdam before moving to Leiden where he is now Professor of Clinical Biostatistics and Medical Decision Making and chair of the Department of Biomedical Data Sciences at Leiden University Medical Center. His research has covered a broad range of methodological and medical topics, which is reflected in hundreds of peer-reviewed methodological and applied publications. His methodological expertise is in the design and analysis of randomized controlled trials, cost-effectiveness analysis, and decision analysis. His methodological research focuses on the development, validation and updating of prediction models, as reflected in a textbook (Springer, 2009). His medical fields of application include oncology, cardiovascular disease, internal medicine, pediatrics, infectious diseases, neurology, surgery and traumatic brain injury.
The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.
There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability.
The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling.
Updates to this new and expanded edition include:
A discussion of Big Data and its implications for the design of prediction models
Machine learning issues
More simulations with missing y values
Extended discussion on between-cohort heterogeneity
Description of ShinyApp
Updated LASSO illustration
New case studies
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 8,13 für den Versand von Vereinigtes Königreich nach Deutschland
Versandziele, Kosten & DauerEUR 5,72 für den Versand von Vereinigtes Königreich nach Deutschland
Versandziele, Kosten & DauerAnbieter: WeBuyBooks, Rossendale, LANCS, Vereinigtes Königreich
Zustand: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind. Artikel-Nr. wbs4812233015
Anzahl: 1 verfügbar
Anbieter: Speedyhen, London, Vereinigtes Königreich
Zustand: NEW. Artikel-Nr. NW9783030163983
Anzahl: 3 verfügbar
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Artikel-Nr. GB-9783030163983
Anzahl: 3 verfügbar
Anbieter: PBShop.store US, Wood Dale, IL, USA
HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Artikel-Nr. GB-9783030163983
Anzahl: 3 verfügbar
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Artikel-Nr. ria9783030163983_new
Anzahl: Mehr als 20 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of avalid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability.The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling.Updates to this new and expanded edition include:-A discussion of Big Data and its implications for the design of prediction models-Machine learning issues-More simulations with missing 'y' values-Extended discussion on between-cohort heterogeneity-Description of ShinyApp-Updated LASSO illustration-New case studies. Artikel-Nr. 9783030163983
Anzahl: 2 verfügbar
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: Neu. Neuware -The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of avalid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability.The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling.Updates to this new and expanded edition include:¿ A discussion of Big Data and its implications for the design of prediction models¿ Machine learning issues¿ More simulations with missing ¿y¿ values¿ Extended discussion on between-cohort heterogeneity¿ Description of ShinyApp¿ Updated LASSO illustration¿ New case studiesSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 592 pp. Englisch. Artikel-Nr. 9783030163983
Anzahl: 2 verfügbar
Anbieter: moluna, Greven, Deutschland
Zustand: New. Ewout Steyerberg worked for 25 years at Erasmus Medical Center in Rotterdam before moving to Leiden where he is now Professor of Clinical Biostatistics and Medical Decision Making and chair of the Department of Biomedical Data Sciences at Leiden Univ. Artikel-Nr. 275832444
Anzahl: 3 verfügbar
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. Artikel-Nr. 370695411
Anzahl: 1 verfügbar
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Hardcover. Zustand: Brand New. 2nd edition. 594 pages. 9.25x6.10x1.38 inches. In Stock. Artikel-Nr. x-3030163989
Anzahl: 2 verfügbar