Abstract
Calibration of clinical prediction models often entails assessing goodness of fit with independent, non-identically distributed Bernoulli random variables. We here investigate two statistics studied by Copas in this setting. We present distribution theory and a simulation study to compare the operating characteristics of the Copas statistics. In our simulation study with relatively small sample sizes, we found a simple Cornish-Fisher approximation tail quantiles of the distributions of the Copas statistics to perform adequately. Upon illustrating their use in a calibration study relating to prediction of atherosclerotic cardiovascular disease risk, power properties appear to reflect differential weighting accorded to observations, as evinced with other goodness-of-fit statistics. The Copas statistics are easily implemented, have proven value in other contexts, and appear to be underutilized in calibration studies. They ought to be part of the armamentarium of calibration tools for all researchers.
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