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Table 2 Results of the segmented linear regression models for the volume of winning and non-winning products

From: The impacts of Chinese drug volume-based procurement policy on the use of policy-related antibiotic drugs in Shenzhen, 2018–2019: an interrupted time-series analysis

  Coefficient Standard Error t p-value 95 % CI
Lower Upper
Model 1, Winning products
Secular trend, β1 3.94 3.28 1.20 0.245 -2.92 10.80
Change in level, β2 185.07 50.14 3.69 0.002 80.13 290.02
Change in trend, β3 -0.82 8.01 -0.10 0.920 -17.58 15.95
Cold, β4 73.25 31.36 2.34 0.031 7.61 138.89
Constant, β0 87.74 30.10 2.92 0.009 24.75 150.73
Model 2, Non-winning products
Secular trend, β1 0.33 0.98 0.33 0.744 -1.73 2.38
Change in level, β2 -26.35 15.03 -1.75 0.096 -57.81 5.11
Change in trend, β3 -4.27 2.40 -1.78 0.091 -9.30 0.75
Cold, β4 23.97 9.41 2.55 0.020 4.27 43.66
Constant, β0 59.08 9.02 6.55 0.000 40.21 77.95
  1. Model 1, F = 25.18, p-value < 0.001, R2 = 0.841, Adjusted R2 = 0.808; Model 2, F = 13.02, p-value < 0.001, R2 = 0.733, Adjusted R2 = 0.676