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Table 2 Observed vs predicted goodness of fit statistics

From: The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

MappingSource of utility dataNObserved mean (95% CI)Predicted mean (95% CI)O-PMean absolute error (MAE)Root mean squared error (RMSE)
Young et al. (2015) [12]Taken from the source paper7710.579 (0.555, 0.603)0.573 (0.552, 0.594)0.0070.134NR
Mapped to AURA data via ‘crosswalk’43820.799 (0.793, 0.805)0.777 (0.771, 0.783)0.0220.0870.119
Khan and Morris (2014) [13]Taken from the source paper20380.610 (0.597, 0.623)0.608 (0.600, 0.616)0.0020.100.09
Mapped to AURA data via ‘crosswalk’43820.799 (0.793, 0.805)0.67 (0.668, 0.672)0.1290.1760.211
Khan et al. (2016) [14]Taken from the source paper9850.515 (0.496, 0.534)0.518 (0.507, 0.529)−0.0030.0990.113
Mapped to AURA data via ‘crosswalk’43820.799 (0.793, 0.805)0.677 (0.676, 0.678)0.1220.1780.219
  1. Key: N Number of questionnaires completed, NR Not reported, O-P Observed mean utility minus predicted mean utility