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| Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating |
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※해외주문 도서 안내 :
- 해외 및 국내 입고 사항에 따라 2~8주 이상 소요될 수 있으며. 취소 및 반품이 불가능합니다.
- 해외주문도서는 해외 출판사 사정에 의해 품절/지연될수 있습니다. |
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※교환 및 반품안내 :
- 교환이나 반품시 상품 수령후 3일이내 교환이나 반품 의사를 알려주시고 일주일이내에 저희쪽으로 상품이 입고되어야합니다.
시일이 경과되면 반품이 불가합니다.
- 변심에 의한 교환이나 반품시, 배송비는 고객님께서 부담하시며, 오배송이나 상품 불량시에는 무료 교환이 가능합니다.
- 반품시에는 반품상품 검수후 카드 취소를 해드리거나 통장,적립금등으로 환불 조치 해 드립니다. |
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※배송안내 :
- 제주도및 도서산간지역은 택배비가 추가될수 있습니다. |
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Prediction models are important in various fields, including medicine, physics, meteorology, and finance. Prediction models will become more relevant in the medical field with the increase in knowledge on potential predictors of outcome, e.g. from genetics. Also, the number of applications will increase, e.g. with targeted early detection of disease, and individualized approaches to diagnostic testing and treatment. The current era of evidence-based medicine asks for an individualized approach to medical decision-making. Evidence-based medicine has a central place for meta-analysis to summarize results from randomized controlled trials; similarly prediction models may summarize the effects of predictors to provide individu- ized predictions of a diagnostic or prognostic outcome.
Why Read This Book? My motivation for working on this book stems primarily from the fact that the development and applications of prediction models are often suboptimal in medical publications. With this book I hope to contribute to better understanding of relevant issues and give practical advice on better modelling strategies than are nowadays widely used. Issues include: (a) Better predictive modelling is sometimes easily possible; e.g. a large data set with high quality data is available, but all continuous predictors are dich- omized, which is known to have several disadvantages.
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Introduction
Applications of prediction models
Study design for prediction models
Statistical models for prediction
Overfitting and optimism in prediction models
Choosing between alternative statistical models
Dealing with 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 methods
Evaluation of performance
Clinical usefulness
Validation of prediction models
Presentation formats
Patterns of external validity
Updating for a new setting
Updating for a multiple settings
Prediction of a binary outcome: 30-day mortality after acute myocardial infarction
Case study on survival analysis: Prediction of secondary cardiovascular events
Lessons from case studies |
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등록된 서평이 없습니다. |
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과별도서목록
