  To evaluate the calibration of a disease risk prediction tool, the quantity
$E/O$, i.e., the ratio of the expected number of events to the observed number
of events, is generally 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 the previously mentioned biases. As a concluding example, we
evaluate the calibration of an existing predictive model for breast cancer on
the E3N-EPIC cohort.
