ABSTRACT
Often, studies modeling an exposure's influence on time to disease-specific death from study enrollment are incorrectly interpreted as if based on time to death from disease diagnosis. We studied 151,996 post-menopausal women without breast or colorectal cancer in the Women's Health Initiative with weight and height measured at enrollment (1993-1998). Using Cox regression models, we contrast hazard ratios (HR) from two time-scales and corresponding study subpopulations: time to cancer death after enrollment among all women and time to cancer death after diagnosis among only cancer survivors. Median follow-up from enrollment to diagnosis/censoring was 13-years for both breast (7633 cases) and colorectal cancer (2290 cases). Follow-up from diagnosis to death/censoring was 7-years for breast and 5-years for colorectal cancer. In analyses of time from enrollment to death, body mass index (BMI)≥35-kg/m2 versus 18.5-<25-kg/m2 was associated with higher rates of cancer mortality: HR=1.99; 95%CI: 1.54, 2.56 for breast cancer (p-trend <0.001) and HR=1.40; 95%CI: 1.04, 1.88 for colorectal cancer (p-trend=0.05). However, in analyses of time from diagnosis to cancer death, trends indicated no significant association (for BMI≥35-kg/m2, HR=1.25; 95%CI: 0.94, 1.67 for breast [p-trend=0.33] and HR=1.18; 95%CI: 0.84, 1.86 for colorectal cancer [p-trend=0.39]). We conclude that a risk factor that increases disease incidence will increase disease-specific mortality. Yet, its influence on post-diagnosis survival can vary, and requires consideration of additional design and analysis issues such as selection bias. Quantitative tools allow joint modeling to compare an exposure's influence on time from enrollment to disease incidence and time from diagnosis to death. This article is protected by copyright. All rights reserved.
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