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Table 3 Goodness-of-fit results from validation analysis

From: From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation

  Validation I Validation II
  Pooled sample (N = 590) Random sample I (N = 100) Random sample II (N = 300) Random sample III (N = 500)
  Mean utility MAE RMSE Mean utility MAE RMSE Mean utility MAE RMSE Mean utility MAE RMSE
  (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Observed 0.8082 0.8265 0.8094 0.8102
Method 1: Ordinary least squares estimator
Model 1 0.8085 0.0982 0.1245 0.8166 0.0874 0.1091 0.8107 0.0938 0.1205 0.8111 0.0985 0.1248
Model 2 0.8088 0.0963** 0.1209* 0.8127 0.0845** 0.1054** 0.8112 0.0943 0.1187* 0.8104 0.0947** 0.1194*
Method 2: Censored least absolute deviations estimator
Model 1 0.8202 0.0993 0.1268 0.8274 0.0867 0.1084 0.8211 0.0931* 0.1209 0.8214 0.0977 0.1253
Model 2 0.8207 0.1006 0.1273 0.8378 0.0866 0.1097 0.8358 0.0946 0.1227 0.8344 0.0954 0.1232
Method 3: MM-estimator
Model 1 0.8133 0.0983 0.1253 0.8232 0.0865 0.1082 0.8164 0.0931* 0.1208 0.8167 0.0977 0.1253
Model 2 0.8147 0.0962* 0.1216** 0.8201 0.0842* 0.1053* 0.8181 0.0937** 0.1193** 0.8168 0.0944* 0.1200**
Method 4: Generalised linear model
Model 1 0.8082 0.0977 0.1243 0.8149 0.0881 0.1097 0.8104 0.0940 0.1215 0.8108 0.0984 0.1252
Model 2 0.8085 0.0979 0.1226 0.8104 0.0920 0.1144 0.8085 0.0964 0.1206 0.8092 0.0967 0.1211
  1. MAE – mean absolute error; RMSE – root mean squared error.
  2. *denotes the smallest value in the column; **denotes the second smallest value in the column.