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Table 3 Calibration of the existing time-dependent model with and without the trend indicators

From: Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study

   Existing model with trend indicators Existing model without trend indicators
Risk Decile # observed deaths # expected deaths z-score p # expected deaths z-score p
1 5 4.31 0.3298 0.7415 5.37 0.1609 0.8722
2 9 12.75 1.0498 0.2938 13.68 1.2664 0.2054
3 21 24.74 0.7527 0.4517 26.03 0.9853 0.3245
4 36 43.45 1.1298 0.2586 44.59 1.2861 0.1984
5 54 68.90 1.7952 0.0726 69.50 1.8597 0.0629
6 103 108.72 0.5482 0.5836 109.97 0.6646 0.5063
7 174 171.65 0.1796 0.8575 173.49 0.0386 0.9692
8 260 285.35 1.5008 0.1334 285.02 1.4821 0.1383
9 453 474.99 1.0090 0.3130 478.93 1.1850 0.2360
10 1525 1496.72 0.7309 0.4649 1497.23 0.7177 0.4729
Total 2640 2691.59 0.9943 0.3201 2703.82 1.2274 0.2197
  1. We divided the validation admissions into risk deciles on each admission day (based on the patient's risk score for that day from the model with the trend indicators), determined the number of observed and expected deaths from each model within each risk decile on each day (where the daily number of expected deaths was equal to the sum of the daily hazard of all patients within each decile), and finally summed the number of observed and expected deaths within each risk decile across all admission days (shown above). We tested for a significant difference between the observed and expected number of deaths within each decile by calculating the p-value associated with standardized z-statistic, where z = (observed-expected)/(√expected).