Sheehan Nuala A, Meng Sha, Didelez Vanessa
Department of Health Sciences, University of Leicester, Leicester, UK.
Methods Mol Biol. 2011;713:153-66. doi: 10.1007/978-1-60327-416-6_12.
Detection and assessment of the effect of a modifiable risk factor on a disease with view to informing public health intervention policies are of fundamental concern in aetiological epidemiology. In order to have solid evidence that such a public health intervention has the desired effect, it is necessary to ascertain that an observed association or correlation between a risk factor and a disease means that the risk factor is causal for the disease. Inferring causality from observational data is difficult, typically due to confounding by social, behavioural, or physiological factors which are difficult to control for and particularly difficult to measure accurately. A possible approach to inferring causality when confounding is believed to be present but unobservable, as it may not even be fully understood, is based on the method of instrumental variables and is known under the name of Mendelian randomisation if the instrument is a genetic variant. While testing for the presence of a causal effect using this method is generally straightforward, point estimates of such an effect are only obtainable under additional parametric assumptions. This chapter introduces the concept and illustrates the method and its assumptions with simple real-life examples. It concludes with a brief discussion on pitfalls and limitations.
检测和评估可改变的风险因素对疾病的影响，以便为公共卫生干预政策提供依据，这是病因流行病学的根本关注点。为了有确凿的证据证明这种公共卫生干预具有预期效果，有必要确定风险因素与疾病之间观察到的关联或相关性意味着该风险因素是该疾病的病因。从观察数据推断因果关系很困难，通常是由于社会、行为或生理因素的混杂，这些因素难以控制，尤其难以准确测量。当认为存在混杂但无法观察到时（因为甚至可能尚未完全理解），推断因果关系的一种可能方法是基于工具变量法，如果工具是基因变异，则称为孟德尔随机化。虽然使用这种方法检验因果效应的存在通常很简单，但只有在额外的参数假设下才能获得这种效应的点估计。本章介绍了这一概念，并用简单的实际例子说明了该方法及其假设。最后简要讨论了陷阱和局限性。
