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Table 1 Determinants of annual healthcare costs, mean annual predictions and cost ratios (patients with vs. without diabetes), Root Mean Squared Errors (RMSE), by several data modeling approaches

From: Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry

Model   Diabetes (CI 95 %) Cost (€) per person/year, patients with diabetes (N = 33,792) CI 95 %a Cost (€) per person/year, patients without diabetes (N = 863,122) CI 95 %a Cost Ratio (with vs. without diabetes) RMSE
One-part models
  Normal (€) 1,832.76 1,795.56–1,869.95 3348.6 3343.8–3353.9 831.2 829.8–832.4 4.03 3,342.4
  Lognormal b (exp β) 6.0 5.84–6.16 6146.5 6116.9–6178.6 1343.6 1340.5–1347.0 4.57 3,670.0
  Gamma (exp β) 2.6 2.56–2.67 3878.1 3867.0–3891.1 826.1 824.8–827.3 4.69 3,351.1
Two-part models
Part 1
  Logistic (OR) 2.40 2.18–2.64      -  
Part 2
  Normal (€) 1,710.36 1,668.40–1,752.32 3392.0 3387.2–3397.5 1058.8 1057.4–1060.4 3.20 3,732.2
  Lognormal (exp β) 3.3 3.21–3.32 4119.9 4104.4–4136.3 1175.2 1173.2–1177.6 5.60 3,760.6
  Gamma (exp β) 2.2 2.21–2.28 3700.1 3690.0–3711.1 1050.8 1049.5–1052.4 3.50 3,735.6
Two part model (logistic + gamma)   3662.26 3652.07–3673.25 891.9 890.63–893.54 4.10 3,739.8
  1. aderived by boostrapping method
  2. bthe log transformed outcome variable was (cost + 1)