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.

中文翻译：

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用于测试连续时间内生命历程流行病学模型的贝叶斯函数方法

背景 生命历程流行病学研究生命不同阶段的重复风险测量与健康结果之间的关联。然而，实证研究通常基于离散时间模型，该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），它将重复的、离散的风险测量视为不可观察的连续过程，以及（ii）分配数据与生命历程流行病学概念模型相对应的概率的测试程序（关键期、敏感期和积累模型）。通过模拟评估 fRLM 的性能，并通过将体重指数 (BMI) 与慢性肾病、炎症和乳腺癌的 mRNA-seq 特征相关的经验应用来说明该方法。结果模拟表明，fRLM 通过对 400 名受试者进行三到五次风险重复评估，确定了正确的生命历程模型。实证例子表明，慢性肾病反映了一个关键时期的过程，而炎症和乳腺癌可能反映了敏感时期的机制。结论 所提出的 fRLM 将风险的重复测量视为连续过程，并且在实际数据场景下，该方法提供了数据与生命历程流行病学常用研究模型相对应的准确概率。fRLM 使用公开软件实现。