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优秀因果关系研究的特征。

Characteristics of good causation studies.

作者信息

Daya Salim

机构信息

Department of Obstetrics and Gynaecology, McMaster University, Hamilton, Ontario, Canada.

出版信息

Semin Reprod Med. 2003 Feb;21(1):73-83. doi: 10.1055/s-2003-39997.

Abstract

The study of causal relationships is important when addressing questions of efficacy of treatment interventions and etiology of disease. The evaluation of a cause-and-effect relationship between exposure to a putative causal factor and outcome can be undertaken using a variety of study designs including randomized controlled trial and cohort and case control studies. Study participants should be selected in a manner that minimizes bias and confounding and is representative of the target population. Confounding can be controlled by using several strategies including restriction, randomization, stratification, matching, and multivariable analyses. The degree of association is then summarized by the relative risk for prospective studies and the odds ratio for retrospective studies. The precision of these estimates should be indicated by providing their confidence intervals. Important indicators of causation are correct temporal and dose-response relationships between exposure and outcome, a large magnitude in the strength of association, and consistency and specificity of association. Biological and epidemiological sensibility and analogy to other well-established relationships provide additional support for a causal hypothesis.

摘要

在探讨治疗干预措施的疗效问题和疾病病因时,因果关系的研究至关重要。对于假定的因果因素暴露与结果之间的因果关系评估,可采用多种研究设计,包括随机对照试验、队列研究和病例对照研究。研究参与者的选择方式应尽量减少偏倚和混杂因素,且能代表目标人群。可通过多种策略控制混杂因素,包括限制、随机化、分层、匹配和多变量分析。前瞻性研究用相对危险度、回顾性研究用比值比来总结关联程度。应通过提供置信区间来表明这些估计值的精确度。因果关系的重要指标包括暴露与结果之间正确的时间和剂量反应关系、关联强度大、关联的一致性和特异性。生物学和流行病学的合理性以及与其他已确立关系的类比,为因果假设提供了额外支持。

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