Table 3.
Cox regression analysis including mutational status, sex, age, and tobacco consumption
| Variable | HR | 95% CI | P |
|---|---|---|---|
| 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.