TY - JOUR
T1 - Methodological considerations for disentangling a risk factor's influence on disease incidence versus postdiagnosis survival
T2 - The example of obesity and breast and colorectal cancer mortality in the Women's Health Initiative
AU - Cespedes Feliciano, Elizabeth M.
AU - Prentice, Ross L.
AU - Aragaki, Aaron K.
AU - Neuhouser, Marian L.
AU - Banack, Hailey R.
AU - Kroenke, Candyce H.
AU - Ho, Gloria Y.F.
AU - Zaslavsky, Oleg
AU - Strickler, Howard D.
AU - Cheng, Ting Yuan David
AU - Chlebowski, Rowan T.
AU - Saquib, Nazmus
AU - Nassir, Rami
AU - Anderson, Garnet
AU - Caan, Bette J.
N1 - Funding Information:
Key words: mortality, survival, breast cancer, colorectal cancer, obesity, methods Additional Supporting Information may be found in the online version of this article. Conflict of interest: RTC received consulting fees from Novartis, Amgen, AstraZeneca, and Genentech; payment for lectures from Novartis and AstraZeneca; fees for participation in review activities from Pfizer; and payment for educational activities from Educational Concepts Group and Physician’s Education Resource (PER). Grant sponsor: National Cancer Institute; Grant numbers: UM1CA173642-04, R01CA184953-02, R01CA175011-01; Grant sponsor: National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services; Grant numbers: HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C DOI: 10.1002/ijc.30931 History: Received 11 Mar 2017; Accepted 4 Aug 2017; Online 18 Aug 2017 Correspondence to: Elizabeth M. Cespedes Feliciano, Kaiser Permanente Northern California, Division of Research, 5th Floor, 2000 Broadway, Oakland, CA 94612, USA, Tel.: 11-617-285-3608, E-mail: elizabeth.m.cespedes@kp.org
Funding Information:
1Division of Research, Oakland, Kaiser Permanente Northern California, CA 2Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 3Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY 4Department of Occupational Medicine, Epidemiology & Prevention, Feinstein Institute for Medical Research, Hofstra Northwell School of Medicine, Great Neck, NY 5Nursing & Health Systems, University of Washington, Seattle, WA 6Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 7Department of Epidemiology, University of Florida, Gainesville, FL 8City of Hope National Medical Center, Duarte, CA 9Department of Epidemiology, Sulaiman AlRajhi College, School of Medicine, Saudi Arabia 10Department of Biochemistry & Molecular Medicine, University of California Davis, Davis, CA
Publisher Copyright:
© 2017 UICC
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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/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 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.
AB - 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/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 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.
KW - breast cancer
KW - colorectal cancer
KW - methods
KW - mortality
KW - obesity
KW - survival
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U2 - 10.1002/ijc.30931
DO - 10.1002/ijc.30931
M3 - Article
C2 - 28833074
AN - SCOPUS:85030690146
VL - 141
SP - 2281
EP - 2290
JO - International Journal of Cancer
JF - International Journal of Cancer
SN - 0020-7136
IS - 11
ER -