Table 5.

Multiple Logistic Regression Analysis for the Association between NFKB2 rs7897947 and Latent Class Membership for the Social Well-being Subscale

Predictors	Odds Ratio	Standard Error	95% CI	Z	p-value
NFKB2 rs7897947	0.46	0.181	0.214, 0.997	−1.97	.049
Age	0.69	0.066	0.567, 0.829	−3.89	<.001
Children at home	4.51	2.371	1.607, 12.638	2.86	.004
Number of comorbid conditions	1.17	0.083	1.015, 1.342	2.17	.030
KPS score	0.57	0.097	0.412, 0.800	−3.27	.001

Overall model fit: χ² = 68.82, p <.0001

Multiple logistic regression analysis of GMM latent classes for social well-being domain QOL scores (0 = higher (n=126), 1 = lower (n=72)). The first three principle components identified from the analysis of ancestry informative markers as well as self-reported race/ethnicity were retained in the model to adjust for potential confounding due to race or ethnicity (data not shown). Predictors evaluated in the model included NFKB2 rs7897947 genotype (TT versus TG + GG)), age (in 5 year increments), children living at home, number of comorbid conditions, and functional status (KPS score in 10 unit increments).

Abbreviations: CI =confidence interval, GMM = growth mixture model, KPS = Karnofsky Performance Status, NFKB2 = nuclear factor kappa beta 2, QOL = quality of life