https://scholars.lib.ntu.edu.tw/handle/123456789/521903
標題: | Weighing the Causal Pies in Case-Control Studies | 作者: | Liao S.-F. WEN-CHUNG LEE |
公開日期: | 2010 | 卷: | 20 | 期: | 7 | 起(迄)頁: | 568-573 | 來源出版物: | Annals of Epidemiology | 摘要: | Purpose: 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. Methods: 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. Results: The CPWs succinctly quantify the relative importance of different classes of causal pies. Conclusions: 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. ? 2010 Elsevier Inc. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953560683&doi=10.1016%2fj.annepidem.2010.04.003&partnerID=40&md5=5419ba5336ab4bf9417bfc889264fa5d https://scholars.lib.ntu.edu.tw/handle/123456789/521903 |
ISSN: | 1047-2797 | DOI: | 10.1016/j.annepidem.2010.04.003 | SDG/關鍵字: | article; bootstrapping; case control study; causal modeling; causal pie weight; hypertension; hypothesis; intervention study; methodology; obesity; priority journal; public health; risk factor; smoking; epidemiology; human; regression analysis; risk; statistical model; Case-Control Studies; Causality; Epidemiologic Methods; Humans; Models, Statistical; Odds Ratio; Regression Analysis; Risk Factors; Case-Control Studies; Causality; Epidemiologic Methods; Humans; Models, Statistical; Odds Ratio; Regression Analysis; Risk Factors |
顯示於: | 流行病學與預防醫學研究所 |
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