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[泌尿外科研究中的因果关系]

[Causality in urologic research].

作者信息

Carrasco Asenjo Miguel

机构信息

Universidad Autónoma de Madrid, Centro Universitario de Salud Pública C/General Oraa 39 28006 Madrid, España.

出版信息

Arch Esp Urol. 2003 Jul-Aug;56(6):577-88.

Abstract

Clinical-epidemiological research may orient us about the causes of disease, the relationships among them, and the relative magnitudes of their effects. The objective of this article is to link the notion of cause with the basic clinical-epidemiological parameters. There are different models explaining causality. All of them present the possible etiologic explanations for the diseases, taking into consideration the current knowledge at the time they have been posed. We start from a purely determinist conception, understanding causality as a constant connection between two factors x and y, unique, and perfectly predictable. Currently, this model is inadequate to be applied to many diseases. Many researchers have modified the determinist model to explain the multiple causality of disease, posing the existence of associations of causal factors, more than single factors, being these associations treated as sufficient cause (i.e. as a group of minimal conditions and events that inevitably produce the disease). That determinist concept of causality is supplemented with the probabilistic concept. The theory of probability is used in it, as well as the related statistical, methods, to empirically evaluate a possible association that is believed causal. As a consequence of the lack of certainty of the prediction at the individual level, the theoretical notion of cause is replaced by the empirical concept of risk factor, referring to a variable which is considered to be related to the probability that one individual develops the disease. Causal inference in epidemiology is the logic development of a theory, based on observations and arguments that attribute the presence (association) of a disease to one or more risk factors. We will follow the principles posed by B. Hill for the complex process called scientific generalization. To correctly perform this relationship between our ideas and are observations it is absolutely important to start from a correct election of the study design with which the research is undertaken.

摘要

临床流行病学研究可以使我们了解疾病的病因、病因之间的关系及其影响的相对程度。本文的目的是将病因的概念与基本的临床流行病学参数联系起来。有不同的模型来解释因果关系。所有这些模型都考虑到提出时的现有知识,给出了疾病可能的病因解释。我们从一个纯粹的决定论概念出发,将因果关系理解为两个因素x和y之间恒定、唯一且完全可预测的联系。目前,这个模型不足以应用于许多疾病。许多研究人员对决定论模型进行了修改,以解释疾病的多重因果关系,提出存在因果因素的关联,而不仅仅是单一因素,这些关联被视为充分病因(即一组必然导致疾病的最小条件和事件)。决定论的因果概念辅以概率概念。其中使用了概率论以及相关的统计方法,以实证评估一种被认为具有因果关系的可能关联。由于个体层面预测缺乏确定性,病因的理论概念被风险因素的实证概念所取代,风险因素是指一个被认为与个体患疾病概率相关的变量。流行病学中的因果推断是一种理论的逻辑发展,它基于将疾病的存在(关联)归因于一个或多个风险因素进行观察和论证。我们将遵循B.希尔提出的原则,用于这个被称为科学归纳的复杂过程。为了正确地在我们的想法和观察之间建立这种关系,从正确选择进行研究的研究设计开始绝对重要。

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