Borrow it
Coverart for item
The Resource Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating, by Ewout W. Steyerberg, (electronic resource)

Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating, by Ewout W. Steyerberg, (electronic resource)

Creator
Summary
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 .--
Language
eng
Work
Publication
Edition
2nd ed. 2019.
Extent
1 online resource (XXXIII, 558 pages)
Contents
  • Introduction
  • Applications of prediction models.Study design for prediction modeling
  • Statistical Models for Prediction
  • Overfitting and optimism in prediction models
  • Choosing between alternative statistical models
  • Missing values
  • Case study on dealing with missing values
  • Coding of Categorical and Continuous Predictors
  • Restrictions on candidate predictors
  • Selection of main effects
  • Assumptions in regression models: Additivity and linearity
  • Modern estimation methods
  • Estimation with external information
  • Evaluation of performance
  • Evaluation of Clinical Usefulness
  • Validation of Prediction Models
  • Presentation formats
  • Patterns of external validity
  • Updating for a new setting
  • Updating for multiple settings
  • Case study on a prediction of 30-day mortality
  • Case study on Survival Analysis: prediction of cardiovascular events
  • Overall lessons and data sets
  • References
Isbn
9783030163990
Link
http://ezproxy.eui.eu/login?url=https://doi.org/10.1007/978-3-030-16399-0
Instance
Label
Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating
Title
Clinical Prediction Models
Title remainder
A Practical Approach to Development, Validation, and Updating
Statement of responsibility
by Ewout W. Steyerberg
Creator
Subject
Language
eng
Summary
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 .--
Member of
Assigning source
Provided by publisher
http://library.link/vocab/creatorName
Steyerberg, Ewout W
Image bit depth
0
Literary form
non fiction
Nature of contents
dictionaries
Series statement
  • Statistics for Biology and Health,
  • Springer eBooks.
http://library.link/vocab/subjectName
  • Statistics
  • Internal medicine
Label
Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating, by Ewout W. Steyerberg, (electronic resource)
Link
http://ezproxy.eui.eu/login?url=https://doi.org/10.1007/978-3-030-16399-0
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- Applications of prediction models.Study design for prediction modeling -- Statistical Models for Prediction -- Overfitting and optimism in prediction models -- Choosing between alternative statistical models -- Missing values -- Case study on dealing with missing values -- Coding of Categorical and Continuous Predictors -- Restrictions on candidate predictors -- Selection of main effects -- Assumptions in regression models: Additivity and linearity -- Modern estimation methods -- Estimation with external information -- Evaluation of performance -- Evaluation of Clinical Usefulness -- Validation of Prediction Models -- Presentation formats -- Patterns of external validity -- Updating for a new setting -- Updating for multiple settings -- Case study on a prediction of 30-day mortality -- Case study on Survival Analysis: prediction of cardiovascular events -- Overall lessons and data sets -- References
Control code
978-3-030-16399-0
Dimensions
unknown
Edition
2nd ed. 2019.
Extent
1 online resource (XXXIII, 558 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9783030163990
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
226 illustrations, 161 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1114822987
Label
Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating, by Ewout W. Steyerberg, (electronic resource)
Link
http://ezproxy.eui.eu/login?url=https://doi.org/10.1007/978-3-030-16399-0
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- Applications of prediction models.Study design for prediction modeling -- Statistical Models for Prediction -- Overfitting and optimism in prediction models -- Choosing between alternative statistical models -- Missing values -- Case study on dealing with missing values -- Coding of Categorical and Continuous Predictors -- Restrictions on candidate predictors -- Selection of main effects -- Assumptions in regression models: Additivity and linearity -- Modern estimation methods -- Estimation with external information -- Evaluation of performance -- Evaluation of Clinical Usefulness -- Validation of Prediction Models -- Presentation formats -- Patterns of external validity -- Updating for a new setting -- Updating for multiple settings -- Case study on a prediction of 30-day mortality -- Case study on Survival Analysis: prediction of cardiovascular events -- Overall lessons and data sets -- References
Control code
978-3-030-16399-0
Dimensions
unknown
Edition
2nd ed. 2019.
Extent
1 online resource (XXXIII, 558 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9783030163990
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
226 illustrations, 161 illustrations in color.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1114822987

Subject

Member of

Library Locations

    • Badia FiesolanaBorrow it
      Via dei Roccettini 9, San Domenico di Fiesole, 50014, IT
      43.803074 11.283055

Library Links

Structured data from the Bibframe namespace is licensed under the Creative Commons Attribution 4.0 International License by European University Institute. Additional terms may apply to data associated with third party namespaces.
Processing Feedback ...