Studies on the conditional life expectancy of patients with chronic myeloid leukaemia (CML) are lacking. Using data from the Netherlands Cancer Registry, we examined the life expectancy of patients with CML in the Netherlands diagnosed during 1989-2018. As of the early 2010s, the life expectancy of patients with CML who survived several years after diagnosis came narrowly close to the general population's life expectancy, regardless of age. This finding can essentially be ascribed to the introduction and broader application of tyrosine kinase inhibitors (TKIs) and provide optimism to patients with CML who can look forward to a near-normal life expectancy in a modern TKI era.

Objective Measuring the performance of models designed to predict individualized treatment effect is challenging, because the outcomes of two alternative treatments are inherently unobservable in one patient. The C–for–benefit was proposed to measure discriminative ability. We aimed to propose metrics of calibration and overall performance for models predicting treatment effect. Study Design and Setting Similar to the previously proposed C–for–benefit, we defined the observed treatment effect as the difference between outcomes in pairs of matched patients. Thus, we redefined the E–statistics, the logistic loss and the Brier score into metrics for measuring a model's ability to predict treatment effect. In a simulation study, the metric values of deliberately perturbed models were compared to those of the data generating model. To illustrate the performance metrics, different models predicting treatment effect were applied to the data of the Diabetes Prevention Program. Results As desired, performance metric values of perturbed models were consistently worse than those of the optimal model (Eavg–for–benefit≥0.070 versus 0.001, E90–for–benefit≥0.115 versus 0.002, log–loss–for–benefit≥0.757 versus 0.733, Brier–for–benefit≥0.215 versus 0.212). Calibration, discriminative ability, and overall performance of three different models were similar in the case study. Conclusion The proposed metrics are useful to assess the calibration and overall performance of models predicting individualized treatment effect, accessible via (https://github.com/CHMMaas/HTEPredictionMetrics).

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