Statistical models for respiratory disease diagnosis and prognosis

Rory Wolfe, John Carlin

Research output: Contribution to journalReview ArticleOtherpeer-review

Abstract

Risk prediction equations are used in a variety of healthcare settings to provide prognosis for patients with various respiratory conditions. This article provides a review of statistical methods for the development, evaluation and implementation of respiratory disease prediction models. We also consider a second, closely related application of these methods: the creation of equations that describe normal lung function in a particular population and the use of such equations in the diagnosis of abnormal lung function. The methods are illustrated with examples of models that have been developed for use in respiratory medicine and research.

Original languageEnglish
Pages (from-to)541-547
Number of pages7
JournalRespirology
Volume20
Issue number4
DOIs
Publication statusPublished - 1 May 2015

Keywords

  • diagnosis
  • normal equation
  • prediction
  • prognostic model
  • risk
  • validation

Cite this

Wolfe, Rory ; Carlin, John. / Statistical models for respiratory disease diagnosis and prognosis. In: Respirology. 2015 ; Vol. 20, No. 4. pp. 541-547.
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Statistical models for respiratory disease diagnosis and prognosis. / Wolfe, Rory; Carlin, John.

In: Respirology, Vol. 20, No. 4, 01.05.2015, p. 541-547.

Research output: Contribution to journalReview ArticleOtherpeer-review

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