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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/clinical-research
课程评论：没有评论
课程名称：理解临床研究：统计背后的故事 课程概述：如果您曾因医学论文中的“置信区间”或“p值”等术语而感到困惑而跳过结果部分，那么您来对地方了。无论您是临床实践者，想通过阅读研究文章来跟上领域发展，还是医学生，想了解如何开展自己的研究，增强对统计分析和结果的理解都将对您大有裨益。无论您是希望正确理解已发表文献，还是计划进行自己的研究，这门课程都是您的第一步。课程将以易懂的方式介绍常用的统计概念，而不深入繁琐的数学公式，使您在临床文献的世界中开启探索之旅。 本课程可免费注册和参与。完成课程要求后，您将有机会购买结业证书，这也可以很好地激励您学习。同时，课程提供经济援助。 课程内容大纲： 1. **启动阶段：定义研究类型** 本周将为您提供对临床研究结果的直观理解，讨论研究方法和数据收集，重点关注研究类型的选择。 2. **描述数据** 本周我们将进入统计领域，探讨何为t检验、曼-惠特尼U检验、威尔科克森检验、费舍尔精确检验及卡方检验等统计测试，以及它们与数据类型的关系。 3. **建立对统计分析的直观理解** 许多医疗专业人士都熟悉p值的概念。本周将帮助您更深入理解p值，探讨中央极限定理及数据分布。 4. **重要的第一步：假设检验与置信水平** 我们将探讨研究问题的假设及其与伦理和公正研究的关系，同时讨论常用的统计测试及其严格假设。 5. **选择合适的统计测试** 本节将重点介绍t检验及其假设，帮助您识别统计测试的不当使用。 6. **分类数据与结果准确性的分析** 在课程的最后一周，我们将研究测试在检测疾病存在或缺失中的有效性，并理解敏感性、特异性及预测值。 您将通过期末考试检验学习成果，希望您能享受这门课程并提升对临床研究的理解。
Name:Getting things started by defining study types
Description:Welcome to the first week. Here we’ll provide an intuitive understanding of clinical research results. So this isn’t a comprehensive statistics course - rather it offers a practical orientation to the field of medical research and commonly used statistical analysis. The first topics we will look at are research methods and data collection with a specific focus on study types. By the end, you should be able to identify which study types are being used and why the researchers selected them, when you are later reading a published paper.
Name:Describing your data
Description:We finally get started with the statistics. Have you ever looked at the methods and results section of any healthcare research publication and noted the variety of statistical tests used? You would have come across terms like t-test, Mann-Whitney-U test, Wilcoxon test, Fisher’s exact test, and the ubiquitous chi-squared test. Why so many tests you might wonder? It’s all about types of data. This week I am going to tackle the differences in data that determine what type of statistical test we can use in making sense of our data.
Name:Building an intuitive understanding of statistical analysis
Description:There is hardly any healthcare professional who is unfamiliar with the p-value. It is usually understood to have a watershed value of 0.05. If a research question is evaluated through the collection of data points and statistical analysis reveals a value less that 0.05, we accept this a proof that some significant difference was found, at least statistically.In reality things are a bit more complicated than that. The literature is currently full of questions about the ubiquitous p-vale and why it is not the panacea many of us have used it as. During this week you will develop an intuitive understanding of concept of a p-value. From there, I'll move on to the heart of probability theory, the Central Limit Theorem and data distribution.
Name:The important first steps: Hypothesis testing and confidence levels
Description:In general, a researcher has a question in mind that he or she needs to answer. Everyone might have an opinion on this question (or answer), but a researcher looks for the answer by designing an experiment and investigating the outcome. First, we will look at hypotheses and how they relate to ethical and unbiased research and reporting. We'll also tackle confidence intervals which I believe are one of the least understood and often misrepresented values in healthcare research. The most common tests used in the literature to compare numerical data point values are t-tests, analysis of variance, and linear regression. In the last lesson we take a closer look at these tests, but perhaps more importantly, their strict assumptions.
Name:Which test should you use?
Description:The most common statistical test that you might come across in the literature is the t-test. There are, in actual fact, a few t-tests, but the one most are familiar with, is of course, Student’s t-test and its ubiquitous p-value. Not everyone, though, knows that the name Student was actually a pseudonym, used by William Gosset (1876 - 1937). Parametric tests have very strict assumptions that must be met before their use is justified. In this lesson we take a closer look at these tests, but perhaps more importantly, their strict assumptions. Once you know these, you will be able to identify when these tests are used inappropriately.
Name:Categorical data and analyzing accuracy of results
Description:Congratulations! You've reached the final week of the course Understanding Clinical Research. In this lesson we will take a look at how good tests are at picking up the presence or absence of disease, helping us choose appropriate tests, and how to interpret positive and negative results. We’ll decipher sensitivity, specificity, positive and negative predictive values. You'll end of this course with a final exam, to test the knowledge and application you've learned in this course. I hope you've enjoyed this course and it helps your understanding of clinical research.
If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves. If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started! The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.
