Skip to main content

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)