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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/validity-bias-epidemiology
课程评论：没有评论
课程名称：流行病学中的有效性与偏倚 课程概述：流行病学研究能够提供有关疾病频率、潜在原因及现有治疗效果的宝贵见解。选择适当的研究设计是回答这些问题的重要一步，但仅此并不足够。研究结果可能由于多种原因而产生偏倚。该课程介绍了这些因素，并提供了在流行病学研究中处理偏倚的指导。你将学习主要的偏倚类型及其对研究结果的影响，集中探讨混杂因素的概念，并探索在不同研究设计中识别和控制混杂因素的方法。最后，我们将讨论效应修饰现象，这对于理解和解释研究结果至关重要。此外，我们还将广泛讨论流行病学中的因果关系，强调如何利用所学工具判断研究结果是否表明真实的关联关系，以及是否可以被视为因果关系。 课程大纲： 模块一：有效性与偏倚简介 该模块讨论研究结果是否真实反映了样本以及更广泛人群的情况，即研究的有效性。你将学习不同类型的系统性错误或偏倚，并了解如何识别和预防选择偏倚和信息偏倚及其变种。 模块二：混杂 此模块探讨变量间的关联，例如风险因素与疾病之间的关系。现实中，许多其他变量可能影响这种关联，第三个变量的存在可能会放大或掩盖研究的真正关联。这种情况称为混杂，而这是研究者的一大噩梦。你将学习检测混杂的方法，并能够将其应用于实际数据中，以判断是否存在混杂。 模块三：处理混杂 该模块专注于处理混杂，可以在数据收集前的设计阶段或数据分析阶段进行。你将了解主要的处理混杂方法，并通过实际案例学习如何在自己的研究中应用这些方法。同时，我们将简要讨论有向无环图，这是一种识别和控制偏倚及混杂的创新方式。 模块四：效应修饰 这是课程的最后一个模块，讨论当一种暴露对结果的影响在另一个变量的不同水平上有所不同的情况，这称为效应修饰。我们将探讨如何处理效应修饰，并强调混杂与效应修饰之间的区别。课程结束时，我们将重温流行病学中的因果推断，讨论如何在确定关联是否具因果性质之前，全面考虑所有可能的解释。
Name:Module 1: Introduction to Validity and Bias
Description:Every time you conduct a study, the most important questions to ask are whether your results are an accurate reflection of the truth both within your sample and in the broader population of interest. This is called validity of the study and more or less determines if your study is of any value. In this module we will discuss what validity actually means and we will describe the different types of systematic error, or bias that may undermine the validity of a study. You will learn how to identify and prevent selection bias and information bias and their variations.
Name:MODULE 2: Confounding
Description:Studies often focus on the association between two variables; for instance, between a risk factor and a disease. However, reality is usually complex and there are many other variables that may influence this association. Sometimes, the presence of a third variable can either exaggerate the association between the two variables we study or mask an underlying true association. This is called confounding and is any researcher’s nightmare. In this module, you will learn multiple methods to detect confounding in a study, so that you can prepare to deal with it. By the end of the module, you will be able to apply these methods to actual data and conclude whether there is confounding.
Name:MODULE 3: Dealing with Confounding
Description:This module is dedicated to dealing with confounding. Confounding can be addressed either at the design stage, before data is collected, or at the analysis stage. You will learn the main approaches to dealing with confounding and you will see practical examples on how to do this in your own studies. We will also briefly discuss about the Directed Acyclic Graphs, which is a novel way to detect bias and confounding and control for them.
Name:MODULE 4: Effect Modification
Description:This is the final module of the course. We start by discussing what happens when the effect of an exposure on an outcome differs across levels of another variable. This is called effect modification. We will discuss how to approach effect modification and we will highlight the distinction between confounding and effect modification. We will close the course by revisiting causal inference in epidemiology, discussing how we can go through all potential explanations of an association before deciding whether it is of causal nature.
Epidemiological studies can provide valuable insights about the frequency of a disease, its potential causes and the effectiveness of available treatments. Selecting an appropriate study design can take you a long way when trying to answer such a question. However, this is by no means enough. A study can yield biased results for many different reasons. This course offers an introduction to some of these factors and provides guidance on how to deal with bias in epidemiological research. In this course you will learn about the main types of bias and what effect they might have on your study findings. You will then focus on the concept of confounding and you will explore various methods to identify and control for confounding in different study designs. In the last module of this course we will discuss the phenomenon of effect modification, which is key to understanding and interpreting study results. We will finish the course with a broader discussion of causality in epidemiology and we will highlight how you can utilise all the tools that you have learnt to decide whether your findings indicate a true association and if this can be considered causal.
