Table 3.

Cox regression analysis including mutational status, sex, age, and tobacco consumption

VariableHR95% CIP
A.    
 Mutation (yes/no) 1.17 0.97-1.40 .096 
 Age, y 1.11 1.08-1.13 <.001 
 Sex (male) 1.15 0.90-1.45 .259 
 Smoking (1st tertile) 1.02 0.81-1.29 .840 
 Smoking (2nd tertile) 1.22 0.96-1.55 .100 
 Smoking (3rd tertile) 1.71 1.24-2.35 .001 
B.    
 Mutation (yes/no) 1.13 0.94-1.36 .192 
 Sex (male) 1.14 0.90-1.44 .274 
 Smoking (1st tertile) 1.03 0.83-1.30 .769 
 Smoking (2nd tertile) 1.26 0.99-1.59 .052 
 Smoking (3rd tertile) 1.73 1.26-2.37 .001 
VariableHR95% CIP
A.    
 Mutation (yes/no) 1.17 0.97-1.40 .096 
 Age, y 1.11 1.08-1.13 <.001 
 Sex (male) 1.15 0.90-1.45 .259 
 Smoking (1st tertile) 1.02 0.81-1.29 .840 
 Smoking (2nd tertile) 1.22 0.96-1.55 .100 
 Smoking (3rd tertile) 1.71 1.24-2.35 .001 
B.    
 Mutation (yes/no) 1.13 0.94-1.36 .192 
 Sex (male) 1.14 0.90-1.44 .274 
 Smoking (1st tertile) 1.03 0.83-1.30 .769 
 Smoking (2nd tertile) 1.26 0.99-1.59 .052 
 Smoking (3rd tertile) 1.73 1.26-2.37 .001 

In part A, time since blood sample is used as the underlying time scale, whereas age is used as the underlying the time scale in part B, emphasizing the impact of age on overall survival in this elderly cohort. The borderline association of CHIP mutations (P = .096) was not confirmed when age was used as the underlying time scale in the Cox regression analysis.

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