Table I.

Mean VUS and chance of obtaining largest VUS (in parenthesis) for different combination methods.

(n1, n2, n3)	SSD1	SSD2	PSD1	PSD2	Cum-logistic	SW1	SW2	Min–max
(20, 20, 20)	0.9135 (0.194)	— —	0.8916 (0.010)	— —	0.9113 (0.183)	0.9216 (0.511)	0.9100 (0.085)	0.8566 (0.015)
(20, 30, 50)	0.9095 (0.084)	0.9095 (0.080)	0.8883 (0.002)	0.8894 (0.002)	0.9079 (0.158)	0.9147 (0.601)	0.9051 (0.066)	0.8519 (0.008)
(30, 40, 50)	0.9074 (0.084)	0.9074 (0.084)	0.8885 (0.001)	0.8889 (0.001)	0.9088 (0.201)	0.9111 (0.576)	0.9027 (0.050)	0.8504 (0.004)
(50, 50, 50)	0.9057 (0.176)	— —	0.8880 (0.002)	— —	0.9072 (0.242)	0.9082 (0.541)	0.9006 (0.039)	0.8483 (0.001)

Simulation setting: normal data with equal variance Σ₁ =Σ₂ = Σ₃ = 0.7 × I_5×5 + 0.3 × J _5×5.

SSD1, scaled stochastic distance method with $\overline{\mathit{\mu }}$ accounting for unbalanced sample size; SSD2, scaled stochastic distance method with $\overline{\mathit{\mu }}$ accounting no unbalanced information; PSD1, penalized stochastic distance method with $\overline{\mathit{\mu }}$ accounting for unbalanced sample size; PSD2, penalized stochastic distance method with $\overline{\mathit{\mu }}$ accounting no unbalanced information; SW1, step-down procedure (stepwise method proceeding from marker with largest VUS to smallest VUS); SW2, step-up procedure (stepwise method proceeding from marker with smallest VUS to largest VUS); Min–max, min–max approach implemented for three diagnostic categories; Cum-logistic, linear combination coefficients from cumulative logistic regression.