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Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows

背景(考古学) 推论 结果(博弈论) 医学 人口学 体质指数 流行病学 病史 统计 心理学 老年学 计算机科学 病理 外科 生物 社会学 人工智能 古生物学 数理经济学 数学
作者
Maude Wagner,Francine Grodstein,Karen Leffondré,Cécilia Samieri,Cécile Proust‐Lima
出处
期刊:BMC Medical Research Methodology [BioMed Central]
卷期号:21 (1) 被引量:10
标识
DOI:10.1186/s12874-021-01403-w
摘要

Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses' Health Study (19,415 women) to investigate the association between BMI history (collected from midlife) and subsequent cognitive decline after age 70. In conclusion, this approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.
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