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Table 4 Multinomial logistic modeling: predicting EQ-5D index scores from SF-36

From: Deriving a mapping algorithm for converting SF-36 scores to EQ-5D utility score in a Korean population

 

Model 11a

Model 12b

Model 13c

Model 14d

Derivation set

        

R2

0.452-0.611

0.445-0.607

0.455-0.615

0.421-0.576

MAE

0.088

 

0.121

 

0.084

 

0.099

 

AE > 0.05 (%)

32.8

 

49.8

 

32.2

 

34.3

 

AE > 0.1 (%)

25.0

 

45.5

 

23.7

 

26.0

 

RMSE

0.297

 

0.347

 

0.290

 

0.315

 

Predicted EQ-5D index

        

Mean (SD)

0.826

(0.279)

0.731

0.310

0.826

(0.279)

0.829

(0.185)

Min/max

0.171

/1.000

0.171

1.000

0.171

/1.000

0.171

/1.000

Internal validation set

        

MAE

0.097

 

0.119

 

0.092

 

0.101

 

AE > 0.05(%)

35.9

 

48.5

 

37.2

 

35.9

 

AE > 0.1(%)

28.7

 

43.2

 

28.9

 

28.1

 

RMSE

0.312

 

0.344

 

0.304

 

0.315

 

Predicted EQ-5D index

        

Mean (SD)

0.825

(0.288)

0.732

0.313

0.821

(0.292)

0.828

(0.292)

Min/max

0.171

/1.000

0.171

1.000

0.171

/1.000

0.171

/1.000

External validation set

        

MAE

0.085

 

0.125

 

0.075

 

0.084

 

AE > 0.05 (%)

47.2

 

60.2

 

45.5

 

44.7

 

AE > 0.1 (%)

26.0

 

52.0

 

26.0

 

26.8

 

RMSE

0.292

 

0.354

 

0.273

 

0.290

 

Predicted EQ-5D index

        

Mean (SD)

0.883

(0.164)

0.753

0.204

0.892

(0.131)

0.877

(0.170)

Min/max

0.171

/1.000

0.171

1.000

0.151

/1.000

0.171

/1.000

  1. MAE, mean absolute error; AE, absolute error ; RMSE, root mean squared error.
  2. aIndependent variables: PF, RP, BP, GH, VT, SF, RE, MH.
  3. bIndependent variables: PF, BP, SF, RE, MH.
  4. cIndependent variables: PF, BP, GH, SF, RE, MH, PF squared, SF squared, RE squared.
  5. dIndependent variables: PCS, MCS, PCS × PCS, MCS × MCS, PCS × MCS.