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In both cases, we used the average predicted risks over all imputed datasets (see below).
More formally, the Hosmer-Lemeshow test compares observed event rates with average predicted risks, typically using deciles for categories of predicted risk, with statistically significant P values indicating lack of calibration (18).
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Calibration (i.e., how well predicted probabilities agree with actual observed risk) was measured by the Hosmer Lemeshow statistic (goodness-of-fit test and calibration curves) that compares the average predicted risk within subgroups to the proportion that actually develops disease.
The DIGS population had an average predicted risk of 14.3% while our simulated cases had an average predicted risk of 13.8%.
The average predicted risk with the NICE Framingham equation was 14.73% whereas it was 24.08% with QRISK2.
The average predicted risk with the NICE Framingham equation was 15.19% and with QRISK2 was 23.70%.
For those patients in the highest predicted risk decile group, the average predicted risk was 23.9% and the actual prevalence of hospitalisation or death was 24.2%.
The observed prevalence of hospitalisation or death is compared to the average predicted risk among individuals in each of the ten predicted risk groups.
The average predicted risk of mortality for those aged 16 to 19 years on the adult ICU was 3.98% and the actual mortality was comparable at 4%.
Approximations using e.g. average predicted risk may be necessary if Net Instance is to be used to support e.g. a global tax that funds benefits for participants.
Figure 2 plots expected and observed mortality by higher and lower c-index groups (expected mortality rates represent average predicted risk in each hospital).
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