Table 3.
Frequency of pre-diagnostic aspirin use in relation to mortality within strata of the time interval from exposure ascertainment to prostate cancer diagnosis in NIH-AARP and PLCO
Time interval from exposure ascertainment to prostate cancer diagnosis (year)
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---|---|---|---|---|---|---|---|---|---|
<2 years
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2–4 years
|
≥ 5 years
|
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None | Occasional | Daily | None | Occasional | Daily | None | Occasional | Daily | |
Prostate-specific mortality | |||||||||
| |||||||||
AARP | |||||||||
No. of deaths, n=709 | 34 | 92 | 53 | 52 | 84 | 66 | 75 | 156 | 97 |
Model 1, HRs (95%CIs) a | ref | 1.38 (1.05, 1.80) | 1.16 (0.84, 1.62) | ref | 0.96 (0.73, 1.27) | 1.17 (0.88, 1.57) | ref | 0.79 (0.63, 0.99) | 0.94 (0.72, 1.22) |
Model 2, HRs (95%CIs) b | ref | 1.38 (1.06, 1.82) | 1.00 (0.72, 1.40) | ref | 0.96 (0.73, 1.27) | 1.12 (0.83, 1.50) | ref | 0.80 (0.63, 1.00) | 0.91 (0.70, 1.19) |
PLCO total | |||||||||
No. of deaths, n=266 | 45 | 18 | 30 | 42 | 20 | 25 | 35 | 30 | 21 |
Model 1, HRs (95%CIs) c | ref | 0.99 (0.60, 1.64) | 1.29 (0.86, 1.93) | ref | 1.23 (0.76, 1.98) | 1.16 (0.75, 1.79) | ref | 1.23 (0.82, 1.84) | 0.66 (0.41, 1.05) |
Model 2, HRs (95%CIs) d | ref | 1.03 (0.62, 1.71) | 1.32 (0.87, 2.00) | ref | 1.23 (0.76, 1.99) | 1.16 (0.74, 1.80) | ref | 1.19 (0.79, 1.79) | 0.62 (0.39, 1.01) |
| |||||||||
Pooled HRs (95%CIs) e | ref | 1.30 (1.02, 1.65) | 1.12 (0.86, 1.45) | ref | 1.02 (0.80, 1.30) | 1.13 (0.88, 1.44) | ref | 0.87 (0.72, 1.07) | 0.83 (0.66, 1.05) |
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All-cause mortality | |||||||||
| |||||||||
AARP | |||||||||
No. of deaths, n=3640 | 205 | 376 | 310 | 285 | 451 | 444 | 344 | 707 | 518 |
Model 1, HRs (95%CIs) a | ref | 1.05 (0.92, 1.18) | 1.22 (1.07, 1.39) | ref | 0.80 (0.71, 0.90) | 1.17 (1.04, 1.31) | ref | 0.75 (0.68, 0.83) | 0.95 (0.85, 1.06) |
Model 2, HRs (95%CIs) b | ref | 1.08 (0.96, 1.22) | 1.06 (0.93, 1.21) | ref | 0.85 (0.76, 0.96) | 1.05 (0.93, 1.18) | ref | 0.81 (0.73, 0.89) | 0.88 (0.79, 0.99) |
PLCO total | |||||||||
No. of deaths, n=1122 | 171 | 91 | 157 | 158 | 83 | 132 | 147 | 85 | 98 |
Model 1, HRs (95%CIs) c | ref | 1.27 (1.02, 1.60) | 1.60 (1.33, 1.92) | ref | 1.08 (0.85, 1.36) | 1.32 (1.09, 1.60) | ref | 0.89 (0.70, 1.12) | 0.81 (0.65, 1.01) |
Model 2, HRs (95%CIs) d | ref | 1.16 (0.92, 1.45) | 1.47 (1.22, 1.77) | ref | 1.06 (0.84, 1.34) | 1.18 (0.97, 1.44) | ref | 0.91 (0.72, 1.15) | 0.75 (0.60, 0.94) |
| |||||||||
Pooled HRs (95%CIs) e | ref | 1.10 (0.99, 1.22) | 1.18 (1.06, 1.32) | ref | 0.89 (0.80, 0.99) | 1.08 (0.98, 1.20) | ref | 0.82 (0.75, 0.90) | 0.85 (0.77, 0.94) |
Abbreviations: HR, hazard ratio; CI, confidence interval; ref, reference.
Model 1 used age as the time-metric, adjusted for Gleason score (<7 vs. ≥7), tumor stage (localized vs. regional/distant), and primary treatment (no curative treatment, prostatectomy, radiation only, hormone treatment only, radiation and hormone treatment, and other).
Model 2 additionally adjusted for race (Non-Hispanic whites vs. other), marital status (married/cohabiting vs. other), history of cardiovascular disease (ever vs. never), history of diabetes (ever vs. never), BMI (<25, 25–29, and ≥30 kg/m2), smoking status (never, former & quit≥10 years, former & quit<10 years, and current & quit<1 year), history of prostate cancer screening (ever vs. never), and self-reported general health status (excellent/very good, good, and fair or poor).
Model 1 age as the time-metric, adjusted for Gleason score (<7 vs. ≥7), tumor stage (localized vs. regional/distant), primary treatment (prostatectomy, radiation only, hormone treatment only, radiation and hormone treatment, and other), and trial arm (screening vs. standard of care).
Model 2 additionally adjusted for race (Non-Hispanic whites vs. other), marital status (married/cohabiting vs. other), history of cardiovascular disease (ever vs. never), history of diabetes (ever vs. never), BMI (<25, 25–29, and ≥30 kg/m2), and smoking status (never, former & quit≥10 years, former & quit<10 years, and current & quit<1 year).
Pooled HRs and 95%CIs were computed using study-specific results from Model #2 in fixed-effects models.