Skip to main content

Table 4 Evaluation of prediction models by multiple regression analysis

From: Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

Index Partial regression coefficient Standard partial regression coefficient F value p value SE VIF
Broad adherence
1 Secondary prevention -0.048 -0.241 3,820.6 < 0.001 0.001 1.07
2 Rehabilitation intensity -0.250 -0.210 2,740.7 < 0.001 0.005 1.13
3 Guidance -0.057 -0.144 1,413.1 < 0.001 0.002 1.03
4 PDC -0.057 -0.075 366.3 < 0.001 0.003 1.10
5 Overlapping outpatient visits 0.028 0.053 116.4 < 0.001 0.003 1.67
6 Overlapping clinical laboratory and physiological tests 0.012 0.091 343.1 < 0.001 0.001 1.70
7 Medical attendance 0.001 0.261 4,460.5 < 0.001 0.005 1.08
8 Generic drug rate index -0.019 -0.016 17.7 < 0.001 0.004 1.04
Age 0.009 0.032 56.6 < 0.001 0.001 1.25
Sex -0.509 -0.086 518.8 < 0.001 0.022 1.01
Follow-up period 0.051 0.254 3,207.9 < 0.001 0.001 1.41
Constant term 3.421   1,249.7 < 0.001 0.105  
  1. Abbreviations: PDC proportion of days covered, SE standard error, VIF variance inflation factor