Methodological considerations for disentangling a risk factor's influence on disease incidence versus postdiagnosis survival: The example of obesity and breast and colorectal cancer mortality in the Women's Health Initiative. Issue 11 (31st August 2017)

• Record Type: Journal Article
• Authors: Cespedes Feliciano, Elizabeth M.; Prentice, Ross L.; Aragaki, Aaron K.; Neuhouser, Marian L.; Banack, Hailey R.; Kroenke, Candyce H.; Ho, Gloria Y.F.; Zaslavsky, Oleg; Strickler, Howard D.; Cheng, Ting‐Yuan David; Chlebowski, Rowan T.; Saquib, Nazmus; Nassir, Rami; Anderson, Garnet; Caan, Bette J.
• Is Part Of: International journal of cancer. Volume 141:Issue 11(2017:Dec. 01)
• Page Start: 2281
• Page End: 2290
• Publication Date: 2017-08-31
• Subjects: mortality -- survival -- breast cancer -- colorectal cancer -- obesity -- methods; Cancer -- Periodicals; Cancer -- Prevention -- Periodicals; 616.994
• DOI: 10.1002/ijc.30931
• Languages: English
• ISSNs: 0020-7136

Abstract

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 postmenopausal 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 (7, 633 cases) and colorectal cancer (2, 290 cases). Median 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/m 2 versus 18.5–<25 kg/m 2 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/m 2, 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 postdiagnosis 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. Abstract : What's new? If a risk factor increases disease incidence then greater disease‐specific mortality is expected even when no influence on survival is apparent. Appropriately modeling the association of a risk factor with post‐diagnosis survival requires consideration of additional design and analysis issues such as selection bias. The authors used obesity's association with cancer mortality as an illustrative example and found that obesity strongly increased cancer risk, but associations with postdiagnosis survival were apparent only with grade‐2+ obesity. They conclude that obesity prevention might reduce cancer incidence and therefore cancer mortality, but the analysis could not determine how interventions managing obesity in cancer patients influence survival.