Prognostic models based on literature and individual patient data in logistic regression analysis

✍ Scribed by E. W. Steyerberg; M. J. C. Eijkemans; J. C. Van Houwelingen; K. L. Lee; J. D. F. Habbema

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 173 KB
• Volume: 19
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(20000130)19:2<141::aid-sim334>3.0.co;2-o

✦ Synopsis

Prognostic models can be developed with multiple regression analysis of a data set containing individual patient data. Often this data set is relatively small, while previously published studies present results for larger numbers of patients. We describe a method to combine univariable regression results from the medical literature with univariable and multivariable results from the data set containing individual patient data. This &adaptation method' exploits the generally strong correlation between univariable and multivariable regression coe$cients. The method is illustrated with several logistic regression models to predict 30-day mortality in patients with acute myocardial infarction. The regression coe$cients showed considerably less variability when estimated with the adaptation method, compared to standard maximum likelihood estimates. Also, model performance, as distinguished in calibration and discrimination, improved clearly when compared to models including shrunk or penalized estimates. We conclude that prognostic models may bene"t substantially from explicit incorporation of literature data.