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Prognostic models based on literature and individual patient data in logistic regression analysis
Author(s) -
Steyerberg E. W.,
Eijkemans M. J. C.,
Van Houwelingen J. C.,
Lee K. L.,
Habbema J. D. F.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000130)19:2<141::aid-sim334>3.0.co;2-o
Subject(s) - logistic regression , regression , regression analysis , statistics , multivariable calculus , regression diagnostic , data set , computer science , factor regression model , set (abstract data type) , linear regression , mathematics , proper linear model , polynomial regression , control engineering , engineering , programming language
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 coefficients. The method is illustrated with several logistic regression models to predict 30‐day mortality in patients with acute myocardial infarction. The regression coefficients 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 benefit substantially from explicit incorporation of literature data. Copyright © 2000 John Wiley & Sons, Ltd.