PDF(Pubmed)
Abstract：
BACKGROUND: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk.
METHODS: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer.
RESULTS: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms.
CONCLUSIONS: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.
METHODS: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer.
RESULTS: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms.
CONCLUSIONS: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.
摘要：
背景：生命过程流行病学检查生命不同阶段的重复风险测量与健康结果之间的关联。实证研究，然而,通常基于离散时间模型，这些模型假设零星的测量场合完全捕获了潜在的长期连续风险过程。
方法：我们提出（i）功能相关生命历程模型（fRLM），重复治疗，作为未观察到的连续过程的离散风险度量，和(Ii)测试程序，以指定数据对应于生命过程流行病学概念模型的概率(关键时期，敏感期和累积模型)。通过仿真评估了fRLM的性能，并且通过将体重指数（BMI）与慢性肾脏疾病的mRNA-seq特征相关联的经验应用来说明该方法，炎症和乳腺癌。
结果：模拟显示，fRLM通过三到五次重复的风险评估和400名受试者确定了正确的生命历程模型。经验例子表明，慢性肾脏疾病反映了关键时期的过程，炎症和乳腺癌可能反映了敏感期机制。
结论：拟议的fRLM将重复的风险度量视为连续过程，在现实的数据场景下，该方法提供了准确的概率，即数据与通常研究的生命过程流行病学模型相对应。fRLM是用公开可用的软件实现的。
方法：我们提出（i）功能相关生命历程模型（fRLM），重复治疗，作为未观察到的连续过程的离散风险度量，和(Ii)测试程序，以指定数据对应于生命过程流行病学概念模型的概率(关键时期，敏感期和累积模型)。通过仿真评估了fRLM的性能，并且通过将体重指数（BMI）与慢性肾脏疾病的mRNA-seq特征相关联的经验应用来说明该方法，炎症和乳腺癌。
结果：模拟显示，fRLM通过三到五次重复的风险评估和400名受试者确定了正确的生命历程模型。经验例子表明，慢性肾脏疾病反映了关键时期的过程，炎症和乳腺癌可能反映了敏感期机制。
结论：拟议的fRLM将重复的风险度量视为连续过程，在现实的数据场景下，该方法提供了准确的概率，即数据与通常研究的生命过程流行病学模型相对应。fRLM是用公开可用的软件实现的。
