Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating. 2nd ed. 2019

種類:

電子ブック

責任表示:

by Ewout W. Steyerberg

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2019

著者名:

• Steyerberg, Ewout W.
• SpringerLink (Online service)

シリーズ名:

Statistics for Biology and Health ;

ISBN:

9783030163990 [3030163997]    

注記:

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.
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 consid

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