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

Elizabeth M. Cespedes Feliciano, Ross L. Prentice, Aaron K. Aragaki, Marian L. Neuhouser, Hailey R. Banack, Candyce H. Kroenke, Gloria Y.F. Ho, Oleg Zaslavsky, Howard Strickler, Ting Yuan David Cheng, Rowan T. Chlebowski, Nazmus Saquib, Rami Nassir, Garnet Anderson, Bette J. Caan

Research output: Contribution to journalArticle

3 Citations (Scopus)

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/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.

Original languageEnglish (US)
Pages (from-to)2281-2290
Number of pages10
JournalInternational Journal of Cancer
Volume141
Issue number11
DOIs
StatePublished - Dec 1 2017

Fingerprint

Women's Health
Colorectal Neoplasms
Obesity
Breast Neoplasms
Survival
Mortality
Incidence
Breast
Neoplasms
Body Mass Index
Selection Bias
Proportional Hazards Models
Survivors
Joints
Weights and Measures

Keywords

  • breast cancer
  • colorectal cancer
  • methods
  • mortality
  • obesity
  • survival

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

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. / 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; Cheng, Ting Yuan David; Chlebowski, Rowan T.; Saquib, Nazmus; Nassir, Rami; Anderson, Garnet; Caan, Bette J.

In: International Journal of Cancer, Vol. 141, No. 11, 01.12.2017, p. 2281-2290.

Research output: Contribution to journalArticle

Cespedes Feliciano, EM, Prentice, RL, Aragaki, AK, Neuhouser, ML, Banack, HR, Kroenke, CH, Ho, GYF, Zaslavsky, O, Strickler, H, Cheng, TYD, Chlebowski, RT, Saquib, N, Nassir, R, Anderson, G & Caan, BJ 2017, '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', International Journal of Cancer, vol. 141, no. 11, pp. 2281-2290. https://doi.org/10.1002/ijc.30931
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 ; Cheng, Ting Yuan David ; Chlebowski, Rowan T. ; Saquib, Nazmus ; Nassir, Rami ; Anderson, Garnet ; Caan, Bette J. / 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. In: International Journal of Cancer. 2017 ; Vol. 141, No. 11. pp. 2281-2290.
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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/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.",
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