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Latency estimation for chronic disease risk: a damped exponential weighting model
Karin Michels1, Mingyang Song2,3,4,5, Walter C Willett4,5,6
1UCLA Department of Epidemiology, Los Angeles, CA, USA.
European Journal of Epidemiology
|July 31, 2020
Summary
Understanding disease etiology requires identifying critical exposure periods. A new damped exponential weighting model reveals recent BMI predicts ER+/PR+ breast cancer, while cumulative BMI predicts ER-/PR- breast cancer.
Area of Science:
- Epidemiology
- Biostatistics
- Environmental Health
Background:
- Identifying critical exposure windows is key to understanding disease etiology.
- Traditional methods oversimplify time-dependent exposures, using baseline, current, or cumulative averages.
- This limits accurate assessment of environmental factors' impact on chronic disease risk.
Purpose of the Study:
- Introduce a novel damped exponential weighting model for exposure latency analysis.
- Estimate optimal exposure weights across different time intervals.
- Apply the model to investigate BMI and alcohol latency for post-menopausal breast cancer.
Main Methods:
- Developed a damped exponential weighting model for flexible exposure pattern analysis.
- Applied the model to 30-year exposure data from the Nurses' Health Study.
- Conducted simulation studies to validate estimation and hypothesis testing procedures.
Main Results:
- Recent BMI was a stronger predictor for ER+/PR+ breast cancer risk.
- Cumulative high BMI over time predicted ER-/PR- breast cancer risk, with no significant latency.
- Alcohol intake showed a positive association with cumulative intake for ER+/PR+ breast cancer.
Conclusions:
- The damped exponential weighting model provides an easy-to-implement approach for exposure latency analysis.
- Different breast cancer subtypes (ER+/PR+ vs. ER-/PR-) exhibit distinct BMI latency patterns.
- Alcohol's association with breast cancer risk appears cumulative, particularly for ER+/PR+ subtypes.
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