Research Center for Genes, Graduate Institute of Epidemiology, National Taiwan University, Taipei, Taiwan.
Ann Epidemiol. 2010 Jul;20(7):568-73. doi: 10.1016/j.annepidem.2010.04.003.
Epidemiologists are familiar with the concepts of Rothman's causal pies. Using real data the Hoffman study showed recently how to calculate the "proportion of diseased subjects who develop the disease due to classes of sufficient causes" (PDCs). The PDC is actually an attributable-fraction index. It may be specific to a particular risk factor profile but it does not correspond to any given class of causal pies. In this study, we show how to estimate the "causal-pie weights" (CPWs), so that each and every class of causal pies has one and only one CPW attached to it.
To conform to Rothman's model, we apply a non-negative linear odds model to constrain all the odds ratios (ORs) to be equal to or greater than one, and the interactions between them to be additive or superadditive. Based on these constrained ORs, we calculate the population attributable fractions, and then the CPWs. We used a published case-control data to show the methodology.
The CPWs succinctly quantify the relative importance of different classes of causal pies.
The proposed method helps to clarify the multi-factorial and complex interactive effects in disease causation. It also provides important information for designing an efficient public health intervention strategy.
流行病学家熟悉 Rothman 因果关系图的概念。Hoffman 最近的研究使用真实数据展示了如何计算“因充分病因类而患病的患者比例”（PDC）。PDC 实际上是归因分数指数。它可能特定于特定的风险因素特征，但它与任何特定类别的因果关系图都不对应。在这项研究中，我们展示了如何估计“因果关系图权重”（CPW），以便每个因果关系图类都有且仅有一个 CPW 与之相关联。
为了符合 Rothman 的模型，我们应用非负线性比值比模型来约束所有比值比（OR）都等于或大于 1，并且它们之间的相互作用是加性或超加性的。基于这些约束的 OR，我们计算人群归因分数，然后计算 CPW。我们使用已发表的病例对照数据来说明该方法。
CPW 简洁地量化了不同因果关系图类别的相对重要性。
所提出的方法有助于阐明疾病病因中的多因素和复杂交互作用。它还为设计有效的公共卫生干预策略提供了重要信息。
