|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/survival-analysis-r-public-health
课程评论：没有评论
课程名称：公共卫生中的R生存分析 课程概述：欢迎来到公共卫生中的R生存分析课程！本系列的前三门课程涵盖了统计思维、相关性、线性回归和逻辑回归。这一课程将向您展示如何进行生存分析，也即“事件发生的时间”分析，解释在此背景下具有特定含义的常见术语，如风险和审查。通过使用流行的免费软件R，您将学习如何从零开始处理数据集，将其导入R中，进行基本的描述性分析，以了解数据的特点和细节，并从Kaplan-Meier图逐步过渡到多重Cox回归。您将使用来自真实、杂乱的患者级数据（如心力衰竭入院患者）模拟的数据，探索哪些因素可以预测他们的随后死亡率。您还将学习如何测试模型假设及其对数据的拟合，以及一些简单的技巧来解决公共卫生数据中常见的问题。课程中将有小测验和R练习，帮助您检查理解程度。 先决条件：虽然课程提供了一些公式以帮助理解，但您不需要数学学位即可跟进。您需要具备基本的数字计算能力（例如，本课程不涉及微积分）和熟悉图形和表格呈现结果的方式。本系列的前三门课程解释了假设检验、p值、置信区间、相关性和回归等概念，并展示了如何安装R和运行基本命令。在本课程中，我们将简要回顾所有这些核心概念，但如果您对这些概念不熟悉，建议您先学习第一门课程：“公共卫生统计思维”，以及第二门课程关于线性回归的内容，然后再进行本课程的学习。 课程大纲： 1. **Kaplan-Meier图**：学习什么是生存分析，何时使用生存分析，以及如何运行和解释最常见的描述性生存分析方法Kaplan-Meier图及其与生存比较的log-rank检验。重点介绍审查的关键概念。 2. **Cox模型**：学习使用Cox比例风险回归建模，这是最常用的生存分析方法，能够结合多个生存预测变量。您将理解风险和风险集的关键概念，并在本课程的后续部分中有大量机会运行Cox模型。 3. **多重Cox模型**：将简单的Cox模型扩展到多重Cox模型。在此之前，您将对主要变量进行必要的描述性统计，并了解如何处理真实公共卫生数据中可能出现的问题及其解决技巧。 4. **比例假设**：课程的最后部分，您将学习如何评估模型的拟合程度，并检验Cox回归中涉及的主要假设（如比例风险）的有效性。这将包括三种类型的残差，并实践拟合多重Cox回归模型，同时决定包含和排除哪些预测变量，这是拟合任何类型回归模型中的常见挑战。
Name:The Kaplan-Meier Plot
Description:What is survival analysis? You’ll see what it is, when to use it and how to run and interpret the most common descriptive survival analysis method, the Kaplan-Meier plot and its associated log-rank test for comparing the survival of two or more patient groups, e.g. those on different treatments. You’ll learn about the key concept of censoring.
Name:The Cox Model
Description:This week you’ll get to know the most commonly used survival analysis method for incorporating not just one but multiple predictors of survival: Cox proportional hazards regression modelling. You’ll learn about the key concepts of hazards and the risk set. From now and until the end of this course, there’ll be plenty of chance to run Cox models on data simulated from real patient-level records for people admitted to hospital with heart failure. You’ll see why missing data and categorical variables can cause problems in regression models such as Cox.
Name:The Multiple Cox Model
Description:You’ll extend the simple Cox model to the multiple Cox model. As preparation, you’ll run the essential descriptive statistics on your main variables. Then you’ll see what can happen with real-life public health data and learn some simple tricks to fix the problem.
Name:The Proportionality Assumption
Description:In this final part of the course, you’ll learn how to assess the fit of the model and test the validity of the main assumptions involved in Cox regression such as proportional hazards. This will cover three types of residuals. Lastly, you’ll get to practise fitting a multiple Cox regression model and will have to decide which predictors to include and which to drop, a ubiquitous challenge for people fitting any type of regression model.
Welcome to Survival Analysis in R for Public Health! The three earlier courses in this series covered statistical thinking, correlation, linear regression and logistic regression. This one will show you how to run survival – or “time to event” – analysis, explaining what’s meant by familiar-sounding but deceptive terms like hazard and censoring, which have specific meanings in this context. Using the popular and completely free software R, you’ll learn how to take a data set from scratch, import it into R, run essential descriptive analyses to get to know the data’s features and quirks, and progress from Kaplan-Meier plots through to multiple Cox regression. You’ll use data simulated from real, messy patient-level data for patients admitted to hospital with heart failure and learn how to explore which factors predict their subsequent mortality. You’ll learn how to test model assumptions and fit to the data and some simple tricks to get round common problems that real public health data have. There will be mini-quizzes on the videos and the R exercises with feedback along the way to check your understanding. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. The three previous courses in the series explained concepts such as hypothesis testing, p values, confidence intervals, correlation and regression and showed how to install R and run basic commands. In this course, we will recap all these core ideas in brief, but if you are unfamiliar with them, then you may prefer to take the first course in particular, Statistical Thinking in Public Health, and perhaps also the second, on linear regression, before embarking on this one.
