Latency estimation for chronic disease risk: a damped exponential weighting model

Identifying the susceptible period when environmental factors affect disease risk is essential for understanding disease etiology. Most existing epidemiologic studies use oversimplified summaries of time-dependent exposures such as baseline or most current exposure, or the cumulative average of exposure over available follow-up periods. In this paper, we introduce a damped exponential weighting model for estimating optimal exposure weights for different time intervals. This model can accommodate flexible patterns of weights and can be fit using standard software. We applied the model to assess the latency of BMI and alcohol for post-menopausal breast cancer based on 30-year exposure history in the Nurses' Health Study. We have also performed a simulation study to assess the validity of the proposed hypothesis testing and estimation procedures in realistic conditions. We found that the type I error is close to 0.05; the bias in our parameter estimates is low and the coverage probability of interval estimates is close to 0.95. For ER+/PR+ breast cancer we found that recent BMI was a more important predictor of risk than more distant BMI; for ER-/PR- breast cancer, no latency was found and risk was characterized by cumulative high levels of BMI over a long period of time. For alcohol intake, we saw a strong positive association with cumulative intake for ER+/PR+ breast cancer; no significant association was found for cumulative intake or for any latency measure of risk for ER-/PR- breast cancer. Our results underscore the value of an easy-to-implement approach to latency analysis of exposure profiles for chronic disease.