From: Using ordinal outcomes to construct and select biomarker combinations for single-level prediction
Data-generating model | K | Training sample size | Prevalences | Biomarker distributions | Parameters |
---|---|---|---|---|---|
Non-proportional odds | 3 | 200, 400, 800, 1600 | P(D=1)=0.1,0.5 | (X|D=1)∼N(0,2I2) | μ∈{–1,0,1,2,3} |
P(D=K)=0.05,0.3 | (X|D=2)∼N(μ,2I2) | ||||
(X|D=3)∼N(2,2I2) | |||||
5 | 200, 400, 800, 1600 | P(D=1)=0.1,0.5 | (X|D=1)∼N(0,2I2) | μ∈{–1,0,1,2,3} | |
P(D=K)=0.05,0.3 | (X|D=2)∼N(0.5,2I2) | ||||
(X|D=3)∼N(1,2I2) | |||||
(X|D=4)∼N(μ,2I2) | |||||
(X|D=5)∼N(2,2I2) | |||||
Proportional odds | 3 | 200, 400, 800, 1600 | P(D=1)=0.1,0.5 | X1∼N(1,0.25) | (β1,β2)∈{(1,2), |
P(D=K)=0.05,0.3 | X2∼N(1,0.25) | (1,1.5),(−1,1)} | |||
5 | 200, 400, 800, 1600 | P(D=1)=0.1,0.5 | X1∼N(1,0.25) | (β1,β2)∈{(1,2), | |
P(D=K)=0.05,0.3 | X2∼N(1,0.25) | (1,1.5),(−1,1)} |