Am J Epidemiol. 2021 Sep 1;190(9):1898-1907. doi: 10.1093/aje/kwab087.
Life-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression.
生命周期流行病学对于描述和分析疾病发展的复杂病因机制非常有用，但现有的统计方法本质上是验证性的，因为它们依赖于先验的模型指定。这限制了可以进行的因果调查的范围，因为这些方法主要适用于检验那些不质疑我们对健康的既定观点的已知假设，这可能导致确认偏误。我们提出了一种探索性的替代方法。我们的方法不是在数据分析之前指定生命周期模型，而是直接从数据中推断生命周期模型。我们提出的方法扩展了著名的 Peter-Clark (PC) 算法（以其作者命名），用于因果发现，并促进了包含时间信息以从观察数据中推断模型。扩展的算法称为时间 PC。之后，可以对获得的生命周期模型进行有趣的因果假设检验。我们的方法补充了经典的验证性方法，并指导研究人员在新的方向上扩展他们的模型。我们使用一个包含近 3000 名丹麦男性从出生到 65 岁的数据集展示了该方法。使用这个数据集，我们推断了社会经济和与健康相关的因素对抑郁发展的生命周期模型。
