Table 5.

Post-sample predictive validity for 'severity-specific' SF-36 to AQoL algorithms

Data	Model	Group	N	Min	Max	Mean	SD
Observed AQoL	Validation sample	NIHSS = 0	786	-0.04	1.00	0.529	0.334
		NIHSS = 1–5	337	-0.04	1.00	0.440	0.296
		NIHSS ≥ 6	114	-0.04	1.00	0.112	0.205
Predicted AQoL	Subscale-based	NIHSS = 0*	580	-0.05	0.93	0.523	0.266
		NIHSS = 1–5*	334	-0.02	0.92	0.450	0.252
		NIHSS ≥ 6^	112	-1.17	0.68	0.105	0.205
	Item-based	NIHSS = 0*	581	-0.08	0.90	0.532	0.264
		NIHSS = 1–5*	335	-0.16	0.93	0.447	0.261
		NIHSS ≥ 6^	112	-0.21	0.72	0.114	0.150
Mean Absolute Deviation (MAD)	Subscale-based	NIHSS = 0*	580	0.00	0.76	0.137	0.115
		NIHSS = 1–5*	334	0.00	0.73	0.149	0.122
		NIHSS ≥ 6^	112	0.00	1.14	0.125	0.179
	Item-based	NIHSS = 0*	581	0.00	0.78	0.130	0.111
		NIHSS = 1–5*	335	0.00	0.76	0.141	0.114
		NIHSS ≥ 6^	112	0.00	0.74	0.095	0.122

*Predicted values obtained from 'low severity' algorithm. ^Predicted values obtained from 'moderate to severe severity' algorithm