. 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 4.

Percentage of data sets simulated under H₁ with 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	995	25.5	44.8	21.0	8.7
ANOVA	FOCE	968	22.3	43.3	25.5	8.9

Wald	FOCE	878	17.9	41.7	32.7	7.7

LRT	FOCE	923	19.1	47.3	20.5	13.1

AIC	FOCE	962	1.3	31.1	13.1	54.5

AICc	FOCE	962	1.6	35.1	14.7	48.6

CAIC	FOCE	962	28.1	48.5	19.9	3.5

BIC	FOCE	962	21.6	50.3	21.7	6.4

BICc	FOCE	962	11.5	51.7	33.1	13.7

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.

Results obtained with FO are not presented for these strategies because of their poor performance under H0 (table 3).