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Statistical Models for Prediction

Part of the Statistics for Biology and Health book series (SBH)

Background

In this chapter, we consider statistical models for different types of outcomes: binary, unordered categorical, ordered categorical, continuous, and survival data. We discuss common statistical models in medical research such as the linear, logistic, and Cox regression model, and also simpler approaches and more flexible extensions, including regression trees and neural networks. Details of the methods are found in many excellent texts. We focus on the most relevant aspects of these models in a prediction context. All models are illustrated with case studies. In Chap. 6, we will discuss aspects of choosing between alternative statistical models.

Keywords

  • Logistic Regression
  • Respiratory Syncytial Virus
  • Generalize Additive Model
  • Glasgow Outcome Scale
  • Linear Predictor

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2009 Springer Science+Business Media, LLC

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Steyerberg, E. (2009). Statistical Models for Prediction. In: Clinical Prediction Models. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77244-8_4

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