. Author manuscript; available in PMC: 2015 Aug 1.

Published in final edited form as: Eur J Oncol Nurs. 2014 Apr 13;18(4):397–404. doi: 10.1016/j.ejon.2014.03.009

Table 3.

Multiple Logistic Regression Analyses for Interferon Gamma Receptor 1(IFNGR1) rs9376268, Interleukin 6 (L6) rs2069840, and Tumor Necrosis Factor Alpha (TNFA) rs1800750 to Predict Subsyndromal Latent Class Membership

Predictor	Odds Ratio	Standard Error	95% CI	Z	p-value
IFNGR1 Genotype	1.87	0.512	1.097, 3.201	2.30	0.022
Age	0.83	0.050	0.740, 0.938	−3.02	0.003
KPS score	0.71	0.102	0.539, 0.943	−2.37	0.018
Overall model fit: χ² = 27.60, p = 0.0011, R² = 0.0772
IL6 Genotype	3.06	1.511	1.165, 8.054	2.27	0.023
Age	0.83	0.050	0.734, 0.932	−3.11	0.002
KPS score	0.73	0.103	0.553, 0.963	−2.23	0.026
Overall model fit: χ² = 27.84, p = 0.0010, R² = 0.0779
TNFA Genotype	0.13	0.105	0.026, 0.635	−2.52	0.012
Age	0.84	0.051	0.748, 0.948	−2.84	0.005
KPS score	0.69	0.101	0.522, 0.923	−2.51	0.012
Overall model fit: χ² = 31.11, p = 0.0003, R² = 0.0870

Multiple logistic regression analysis of candidate gene associations with resilient versus subsyndromal classes. For each model, the first three principal components identified from the analysis of ancestry informative markers as well as self-report race/ethnicity were retained in all models to adjust for potential confounding due to race or ethnicity (data not shown). Predictors evaluated in each model included genotype (IFNGR1 rs9376268: GG versus GA+AA; IL6 rs2069840: CC+CG versus GG; TNFA rs1799964: TT+TC versus CC), age (in 5 year increments), and functional status at baseline (estimated by the KPS score, in 10 point increments).

Abbreviations; CI = confidence interval; KPS = Karnofsky Performance Status