Health and Quality of Life Outcomes

Table 3 Predictive performance of each mapping algorithm

From: Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer

	Whole sample			ninefold cross-validation
	RMSE	MAE	ρ	RMSE	MAE	ρ
EORTC QLQ-C30
Linear	0.099	0.075	0.838	0.100	0.076	0.833
Beta	0.103	0.081	0.825	0.105	0.081	0.817
Tweedie	0.110	0.084	0.803	0.114	0.086	0.799
Tobit	0.102	0.077	0.836	0.103	0.078	0.822
Two-part linear	0.100	0.075	0.837	0.101	0.076	0.825
Two-part beta	0.099	0.075	0.840	0.101	0.077	0.828
Ordinal logistic	0.100	0.077	0.835	0.101	0.078	0.829
FACT-G
Linear	0.121	0.090	0.753	0.121	0.091	0.744
Beta	0.121	0.091	0.754	0.122	0.092	0.752
Tweedie	0.124	0.092	0.740	0.124	0.093	0.740
Tobit	0.123	0.090	0.754	0.124	0.091	0.751
Two-part linear	0.122	0.091	0.749	0.123	0.092	0.748
Two-part beta	0.119	0.090	0.760	0.121	0.091	0.759
Ordinal logistic	0.119	0.090	0.760	0.120	0.091	0.764

The best performances in each performance measure in each source measure are italics
RMSE, root mean squared error; MAE, mean absolute error; ρ, correlation coefficient; EORTC QLQ-C30, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30; FACT-G, Functional Assessment of Cancer Therapy General

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ISSN: 1477-7525

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