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Table 3 Performance of sickness absence (SA) prediction models

From: External validation of two prediction models identifying employees at risk of high sickness absence: cohort study with 1-year follow-up

 

Development setting

Validation setting

  

Fixed coefficients

Re-estimated coefficients

Gender inclusive

SRH exclusive

SA days model

     

Regression coefficients (SEa)

     

Age

−0.016 (0.015)

−0.016 (0.015)

−0.016 (0.014)

0.004 (0.014)

−0.001 (0.014)

Prior SA

0.007 (0.001)

0.007 (0.001)

0.003 (0.002)

0.003 (0.002)

0.004 (0.002)

Self-rated health

−0.718 (0.244)

−0.718 (0.244)

−0.356 (0.170)

−0.349 (0.173)

not included

Gender

not included

not included

not included

0.699 (0.269)

not included

Predictive performance

     

Nagelkerke’s pseudo R2

0.12

0.03

0.03

0.05

0.02

Discrimination (AUCb)

0.73

0.65

0.68

0.68

0.65

Calibration (slope)

0.94

0.89

0.87

0.86

0.86

SA episodes model

     

Regression coefficients (SEa)

     

Age

−0.043 (0.016)

−0.043 (0.016)

−0.039 (0.015)

0.008 (0.015)

0.005 (0.015)

Prior SA

0.472 (0.070)

0.472 (0.070)

0.465 (0.067)

0.473 (0.068)

0.477 (0.065)

Self-rated health

−0.715 (0.255)

−0.715 (0.255)

−0.190 (0.185)

−0.187 (0.188)

not included

Gender

not included

not included

not included

0.463 (0.256

not included

Predictive performance

     

Nagelkerke’s pseudo R2

0.32

0.18

0.21

0.22

0.21

Discrimination (AUCb)

0.83

0.76

0.78

0.78

0.77

Calibration (slope)

0.98

0.96

0.98

0.95

0.98

  1. a standard error; barea under the receiver operating characteristic curve.
  2. The table shows the regression coefficients and performance measures in a development sample of 535 health care workers and the current validation sample of 593 office workers.