|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistics-in-clinical-trials-part-2/
课程评论：没有评论
**课程名称：** 临床试验中的统计学（第二部分） **课程概述：** 本课程是“临床试验中的统计学”系列的进阶篇，专为临床研究工作者和希望深化医学统计学技能的学习者设计。课程将系统介绍在临床研究中常用的高级统计方法，包括协方差分析（ANCOVA）、逻辑回归和z检验等。 **核心内容：** * **高级统计方法深入解析：** 详细讲解ANCOVA、逻辑回归和z检验等在临床试验中广泛应用的统计模型和测试。 * **RStudio实操演示：** 所有统计方法都将结合RStudio进行实际计算和操作演示，帮助学习者将理论知识应用于实际工作。 * **临床研究中的挑战：** 探讨在实际临床研究中常常被忽视的统计建模挑战。 **目标受众：** * **具备统计学基础的学习者：** 希望扩展统计学知识，特别是针对临床研究应用的学习者。 * **临床研究从业人员：** 希望了解和应用更高级统计方法以提升工作效率的学习者。 * **科学研究人员：** 希望将统计学理论应用于医学和临床研究领域的研究者。 * **有统计学背景但想进入医学领域者：** 希望拓展专业领域，学习医学统计学知识的学习者。 **学习目标：** 通过学习本课程，学员将能够： * 掌握临床试验中常用的高级统计分析方法。 * 熟练运用RStudio进行相关的统计计算和数据分析。 * 理解临床研究中统计建模的实际挑战。 * 提升在临床研究和医学统计学领域的专业知识和实践能力。
Is your work related to clinical research? Or perhaps you plan to develop your skills in medical statistics? If so, this training is just for you! The training "Statistics in Clinical Trials. Part 2" expands on the topics discussed in the first part of this course, delving into more advanced methods. Through this training, you will learn statistical methods that will aid in clinical research work.The second part of the course provides a more detailed discussion on commonly used tests and models in clinical research, including ANCOVA, logistic regression, and z-tests. Each statistical topic is also presented in terms of practical calculations in RStudio.The second part of the training allows you to acquire more advanced knowledge in both clinical research and the statistics involved. The course is designed for individuals who already have a basic understanding of statistics but wish to expand their knowledge and are interested in the models applied in real-world clinical research. The course also addresses the actual challenges of statistical modeling in this field, which are often overlooked in typical statistics courses. We particularly recommend it to individuals involved in clinical research, those who have theoretical knowledge of statistics and want to see its practical application in this field, as well as scientific researchers or individuals with a background in statistics who plan to broaden their knowledge in the areas related to medicine and clinical research.
