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How to evaluate the calibration of a disease risk prediction tool
Author(s) -
Viallon Vivian,
Ragusa Stéphane,
ClavelChapelon Françoise,
Bénichou Jacques
Publication year - 2009
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/sim.3517
Subject(s) - censoring (clinical trials) , computer science , calibration , epic , statistics , econometrics , population , drop out , medicine , mathematics , art , environmental health , literature , economics , demographic economics
To evaluate the calibration of a disease risk prediction tool, the quantity E / O , i.e. the ratio of the expected to the observed number of events, is usually computed. However, because of censoring, or more precisely because of individuals who drop out before the termination of the study, this quantity is generally unavailable for the complete population study and an alternative estimate has to be computed. In this paper, we present and compare four methods to do this. We show that two of the most commonly used methods generally lead to biased estimates. Our arguments are first based on some theoretic considerations. Then, we perform a simulation study to highlight the magnitude of biases. As a concluding example, we evaluate the calibration of an existing predictive model for breast cancer on the E3N–EPIC cohort. Copyright © 2009 John Wiley & Sons, Ltd.