. Author manuscript; available in PMC: 2009 Nov 27.

Published in final edited form as: J Biopharm Stat. 2008;18(6):1084–1102. doi: 10.1080/10543400802369012

Table 3.

Percentage of data sets simulated under H₀ with a design of N = 40 for which each model is selected.

Method	Algorithm	K	Model¹
Method	Algorithm	K	M_base	M_recessive	M_dominant	M_mult
ANOVA	FO	997	91.6	4.1	3.9	0.4
ANOVA	FOCE	986	90.9	3.8	4.7	0.6

Wald	FO	947	68.4	11.1	16.3	4.2
Wald	FOCE	876	83.0	5.8	9.8	1.4

LRT	FO	976	50.3	18.7	17.0	14.0
LRT	FOCE	951	91.3	4.0	3.5	1.2

AIC	FO	999	14.9	23.2	22.4	39.5
AIC	FOCE	970	42.2	22.4	21.3	14.1

AICc	FO	999	17.4	24.4	32.2	35.0
AICc	FOCE	970	48.3	20.7	20.1	9.9

CAIC	FO	999	63.0	16.6	14.5	5.9
CAIC	FOCE	970	95.3	2.1	2.5	0.1

BIC	FO	999	55.7	19.6	16.6	8.1
BIC	FOCE	970	93.3	3.1	3.0	0.6

BICc	FO	999	44.6	21.7	20.0	13.7
BICc	FOCE	970	85.6	7.0	6.0	1.2

K is the number of data sets on which the test could be performed.

M_base: {β₀ = β₁ = β₂ = 1} (CC=CT=TT) model with no gene effect.

M_recessive: {β₀ = β₁ = 1, β₂ ≠ 1} (CC = CT ≠ TT), reduced model.

M_dominant: {β₀ = 1, β₁ = β₂ ≠ 1} (CC ≠ CT = TT), reduced model.

M_mult: {β₀ = 1, β₁ ≠ β₂ ≠ 1} (CC ≠ CT ≠ TT), complete model.