. Author manuscript; available in PMC: 2017 Jun 27.

Published in final edited form as: Biol Res Nurs. 2014 May 27;17(2):175–184. doi: 10.1177/1099800414534313

Table 2. Multiple logistic regression analyses for morning fatigue groups and TNFA candidate genes.

Predictor	Odds Ratio	Standard Error	95% CI	Z	p-value
TNFA rs1800629	0.48	0.157	0.252, 0.910	-2.25	0.025
Age	0.79	0.063	0.673, 0.922	-2.97	0.003
KPS score	0.54	0.102	0.371, 0.782	-3.26	0.001
Overall model fit: χ² = 39.39, p < 0.0001, R² = 0.1342
TNFA rs3093662	6.59	4.369	1.796, 24.171	2.84	0.004
Age	0.76	0.062	0.645, 0.889	-3.39	0.001
KPS score	0.53	0.103	0.365, 0.780	-3.24	0.001
Overall model fit: χ² = 45.43, p < 0.0001, R² = 0.1548

Note. For each model, the first three principal components identified from the analysis of ancestry informative markers as well as self-reported race/ethnicity (White, Asian/Pacific Islander, Black, Hispanic/mixed background/other) were retained in all models to adjust for potential confounding due to race or ethnicity (data not shown). Predictors evaluated in the model included genotype (TNFA rs1800629: GG versus GA+AA; TNFA rs3093662: AA versus AG + GG), age (5-year increments), and functional status (KPS score, 10-point increments). CI = confidence interval; KPS = Karnofsky Performance Status; TNFA = tumor necrosis factor alpha gene.