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Research Methods & Reporting

Prognosis and prognostic research: application and impact of prognostic models in clinical practice

BMJ 2009; 338 doi: https://doi.org/10.1136/bmj.b606 (Published 04 June 2009) Cite this as: BMJ 2009;338:b606
  1. Karel G M Moons, professor of clinical epidemiology1,
  2. Douglas G Altman, professor of statistics in medicine2,
  3. Yvonne Vergouwe, assistant professor of clinical epidemiology1,
  4. Patrick Royston, senior statistician3
  1. 1Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, Netherlands
  2. 2Centre for Statistics in Medicine, University of Oxford, Oxford OX2 6UD
  3. 3MRC Clinical Trials Unit, London NW1 2DA
  1. Correspondence to: K G M Moons k.g.m.moons{at}umcutrecht.nl
  • Accepted 6 October 2008

An accurate prognostic model is of no benefit if it is not generalisable or doesn’t change behaviour. In the last article in their series Karel Moons and colleagues discuss how to determine the practical value of models

Prognostic models are developed to be applied in new patients, who may come from different centres, countries, or times. Hence, new patients are commonly referred to as different from but similar to the patients used to develop the models.1 2 3 4 But what exactly does this mean? When can a new patient population be considered similar (enough) to the development population to justify validation and eventually application of a model? We have already considered the design, development, and validation of prognostic research and models.5 6 7 In the final article of our series, we discuss common limitations to the application and generalisation of prognostic models and what evidence beyond validation is needed before practitioners can confidently apply a model to their patients. These issues also apply to prediction models with a diagnostic outcome (presence of a disease).

Summary points

  • Prognostic models generalise best to populations that have similar ranges of predictor values to those in the development population

  • When a prognostic model performs less well in a new population, using the new data to modify the model should first be considered rather than directly developing a new model

  • Application of prognostic models requires unambiguous definitions of predictors and outcomes and reproducible measurements using methods available in clinical practice

  • Impact studies quantify the effect of using a prognostic model on physicians’ behaviour, patient outcome, or cost effectiveness of care compared with usual care without the model

  • Impact studies require different design, outcome, analysis, and reporting from validation studies

Limitations to application

Extrapolation versus validation

Most prediction models are developed in secondary care, and it is common to want to …

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