Abstract
Semi-parametric Cox regression and parametric methods have been used to analyze survival data of cancer; however, no study has focused on the comparison of survival models in genetic association analysis of age at onset (AAO) of cancer. The Hepatocyte nuclear factor-1- beta (HNF1B) gene has been associated with risk of endometrial and prostate cancers; however, no study has focused on the effect of HNF1B gene on the AAO of cancer. This study examined 23 single nucleotide polymorphisms (SNPs) within the HNF1B gene in the Marshfield sample with 716 cancer cases and 2,848 non-cancer controls. Cox proportional hazards models in PROC PHREG and parametric survival models (including exponential, Weibull, log-normal, log-logistic, and gamma models) in PROC LIFEREG in SAS 9.4 were used to detect the genetic association of HNF1B gene with the AAO. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to compare the Cox models and parametric survival models. Both AIC and BIC values showed that the Weibull distribution is the best model for all the 23 SNPs and the Gamma distribution is the second best. The top two SNPs are rs4239217 and rs7501939 with time ratio (TR) =1.08 (p<0.0001 for the AA and AG genotypes, respectively) and 1.07 (p=0.0004 and 0.0002 for CC and CT genotypes, respectively) based on the Weibull model, respectively. This study shows that the parametric Weibull distribution is the best model for the genetic association of AAO of cancer and provides the first evidence of several genetic variants within the HNF1B gene associated with AAO of cancer.
Highlights
Survival analysis methods, including non-parametric Kaplan-Meier method, semi-parametric Cox proportional hazards model as well as parametric methods have been used in cancer survival studies
We explored the associations of 23 Hepatocyte nuclear factor-1beta (HNF1B) single nucleotide polymorphisms (SNPs) with the age at onset (AAO) of cancer
Using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), the Weibull distribution was found to be the best model for genetic association of polymorphisms within the HNF1B gene with the AAO of cancer
Summary
Survival analysis methods, including non-parametric Kaplan-Meier method (including log-rank test and Wilcoxon test), semi-parametric Cox proportional hazards model as well as parametric methods (such as exponential, Weibull, gamma, log-normal, and log-logistic models) have been used in cancer survival studies. 424 COMPARISON OF COX REGRESSION AND PARAMETRIC MODELS FOR SURVIVAL ANALYSIS OF GENETIC VARIANTS IN HNF1B GENE RELATED TO AGE AT ONSET OF CANCER (Moghimi-Dehkordi et al, 2008). The semi-parametric Cox proportional hazards model has been used to examine the associations of genetic variants with AAO of cancer. Previous studies tested the association between p53 and DNMT3b polymorphisms and AAO of colorectal cancer using the Cox proportional hazards regression model (Jones et al, 2004; Krüger et al, 2005; Sotamaa et al, 2005; Jones et al, 2006). This study was to identify the best model by comparing the Cox proportional hazards models and parametric survival models in genetic association analysis of the HNF1B gene with the AAO of cancer in a Caucasian sample
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