Survival Analysis in R for Public Health from Coursera

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.

What's inside

Syllabus

The Kaplan-Meier Plot

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.

The Cox Model

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.

The Multiple Cox Model

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.

The Proportionality Assumption

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces concepts such as proportional hazards, the risk set, and the Kaplan-Meier plot that are important in survival analysis

Provides hands-on practice by using real patient-level data for patients admitted to hospital with heart failure

Assumes familiarity with hypothesis testing, p values, confidence intervals, correlation and regression prior to taking this course

Covers testing model assumptions and fit to the data, and provides tips to address common problems encountered in real public health data

Emphasizes the analysis and interpretation of survival data, which is particularly relevant for healthcare professionals, researchers, and students in public health

Taught by Alex Bottle, an experienced instructor in the fields of statistics and public health

Reviews summary

Helpful survival analysis r overview

Learners say that this course is an excellent introduction to survival analysis, especially for beginners. Using a combination of videos, readings, and practical R sessions, this well-organized course provides learners with clear explanations of basic survival analysis concepts. Even though it lacks some of the mathematical details, the course provides learners with a good starting point that they can build on in the future.

Very thorough.

"Very clear explanation of the basic concepts needed to approach survival analysis. Highly recommended."

"This course can be improved by fixing mistakes (especially the ones on quizzes), and instructors need to be more active on the forums and help students with questions."

"Great introduction to survival analysis, explaining key concepts in a simple and effective manner."

Suited for beginners.

"A fantasic intro to learn survival analysis where the time to the outcome is important"

"Very nice course to get an introduction on survival analysis in R."

"Minimal prior knowledge required, and a great practical application."

Excellent teaching.

"Nice lecture by the excellent lecturer"

"Excellent course and instructor"

"Great course superb support and very clear professor."

Could go into more detail.

"Was a good overview of the basics of survival analysis, would have liked it to go into more depth"

"Rarely any clear explanation, no formulas at all."

"It made learning very frustrating in every sense."

Technical difficulties reported.

"The feedback on the quizzes is extremely inadequate. Very difficult to understand your mistakes."

"Got some setting error and not yet be fixed in week 4."

"The transcript is poorly made so I could not save notes without translating the transcript. There are bugs in quizzes (wrong model answer) too."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Survival Analysis in R for Public Health with these activities:

Review Probability and Statistics Concepts

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Refreshing these foundational concepts will strengthen the understanding of survival analysis.

Browse courses on Probability

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Review lecture notes or textbook chapters on probability and statistics
Practice solving problems related to these concepts
Take a practice quiz or assessment

Practice R Programming for Survival Analysis

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Refining R programming skills specific to survival analysis will enhance proficiency in data manipulation and analysis.

Browse courses on R Programming

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Complete online tutorials or video lessons on R for survival analysis
Work through practice exercises using R packages like survival and survminer
Create a small R script to perform survival analysis on a dataset

Review: Applied Survival Analysis, Second Edition

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This book provides a comprehensive and practical guide to survival analysis, complementing the course concepts.

View Applied Survival Analysis: Regression Modeling... on Amazon

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Identify relevant chapters or sections
Read and summarize the selected materials
Relate the book content to the course lectures

Six other activities

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Show all nine activities

Summarize Survival Analysis Concepts

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Creating a summary of key concepts will help reinforce understanding and improve retention.

Browse courses on Cox Regression

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Review lecture materials and notes
Identify the main concepts covered in the course
Write a concise summary of each concept
Organize the summary in a logical manner

Discuss Survival Analysis Applications

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Engaging in peer discussions can provide diverse perspectives and enhance comprehension of real-world applications.

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Find a study group or discussion forum
Prepare talking points based on lecture materials
Actively participate in discussions
Share insights and ask clarifying questions

Kaplan-Meier Survival Analysis Calculations

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Practicing calculations related to Kaplan-Meier survival analysis will help solidify the concepts and formulas.

Browse courses on Survival Analysis

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Review lecture materials on Kaplan-Meier survival analysis
Find a dataset with survival data
Calculate the Kaplan-Meier survival curve using this dataset
Plot the Kaplan-Meier survival curve

Cox Proportional Hazards Regression

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Practicing Cox proportional hazards regression will enhance understanding of how to incorporate multiple predictors of survival.

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Review lecture materials on Cox proportional hazards regression
Find a dataset with survival data and associated covariates
Fit a Cox proportional hazards regression model using this dataset
Interpret the results of the Cox regression model

Model Assessment for Survival Analysis

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Practicing model assessment techniques will help refine understanding of how to evaluate the performance and validity of survival analysis models.

Browse courses on Model Assessment

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Review lecture materials on model assessment in survival analysis
Fit a Cox proportional hazards regression model using a dataset with survival data
Calculate and interpret the concordance index (c-index)
Perform a time-dependent ROC curve analysis

Contribute to the survival Package

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Contributing to the survival package will enhance understanding of the underlying algorithms and provide practical experience.

Browse courses on Open Source

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Review the survival package documentation and source code
Identify an area for potential improvement or contribution
Write and test code to implement the improvement
Submit a pull request to the survival package repository

Career center

Learners who complete Survival Analysis in R for Public Health will develop knowledge and skills that may be useful to these careers:

Epidemiologist

Epidemiologists investigate the causes and patterns of health and disease in populations. The Survival Analysis course in R aligns well with this field, as it provides essential knowledge and skills for analyzing survival data, evaluating risk factors, and understanding disease progression. By learning these techniques, you can contribute to public health research and develop effective strategies for disease prevention and control.

See salaries and explore the career path for Epidemiologist

Public Health Researcher

Public Health Researchers conduct research to improve the health and well-being of populations. The Survival Analysis course in R provides you with a valuable skill set for analyzing survival data, identifying risk factors, and evaluating the effectiveness of public health interventions. By gaining expertise in these methods, you can contribute to evidence-based decision-making and develop strategies to address population health challenges.

See salaries and explore the career path for Public Health Researcher

Statistician

Statisticians collect, analyze, interpret, and present data to solve problems and make informed decisions. The Survival Analysis course in R will equip you with valuable statistical knowledge and techniques, enabling you to analyze time-to-event data, assess survival rates, and identify factors that influence outcomes. This expertise will enhance your ability to conduct rigorous statistical analyses and provide evidence-based insights in various fields.

See salaries and explore the career path for Statistician

Biostatistician

Biostatisticians apply statistical methods to solve problems in the biomedical sciences. The Survival Analysis course in R offers a comprehensive introduction to survival analysis techniques, which are widely used in clinical research to analyze patient outcomes, assess treatment effectiveness, and predict prognosis. This specialized knowledge will enhance your ability to design and conduct biomedical studies and contribute to advancements in healthcare.

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Data Scientist

Data Scientists use data to solve complex problems and extract valuable insights. The Survival Analysis course in R will equip you with advanced statistical methods for analyzing survival data, identifying patterns and trends, and making predictions. This expertise will enhance your ability to develop machine learning models and leverage data to drive business outcomes.

See salaries and explore the career path for Data Scientist

Health Economist

Health Economists analyze the costs and benefits of healthcare interventions and policies. The Survival Analysis course in R will equip you with the skills to evaluate the cost-effectiveness of treatments and interventions, assess the impact of healthcare policies on patient outcomes, and identify strategies to optimize resource allocation. This knowledge will enhance your ability to make informed decisions and contribute to efficient healthcare systems.

See salaries and explore the career path for Health Economist

Actuary

Actuaries assess and manage financial risks. The Survival Analysis course in R will provide you with a strong foundation in statistical modeling and risk analysis, which are essential skills for actuaries. By learning survival analysis techniques, you can develop actuarial models to predict the occurrence of events, such as death or disability, and evaluate the financial implications of insurance and pension plans.

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Clinical Research Associate

Clinical Research Associates manage clinical trials and ensure compliance with regulations. The Survival Analysis course in R offers valuable insights into statistical methods used in clinical research, including techniques for analyzing survival data and evaluating treatment outcomes. This knowledge will enhance your understanding of clinical trial design and data analysis, enabling you to contribute effectively to the development of new therapies and treatments.

See salaries and explore the career path for Clinical Research Associate

Data Analyst

Data Analysts translate raw data into meaningful insights that drive better decision-making. Coursework in Survival Analysis in R would provide a solid foundation in statistical analysis, allowing you to analyze large datasets and identify trends and patterns that can inform business strategies. Understanding survival analysis techniques can also help you develop predictive models and forecast future outcomes, making you a valuable asset in this field.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Survival Analysis course in R offers a valuable introduction to statistical methods for analyzing survival data, which can be applied in various financial contexts, such as modeling credit risk, predicting default rates, and evaluating investment performance. This knowledge will enhance your analytical skills and enable you to make informed investment decisions.

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Risk Analyst

Risk Analysts identify, assess, and manage risks in various industries. The Survival Analysis course in R will provide you with a strong foundation in statistical modeling and risk analysis, enabling you to evaluate the likelihood and impact of events, such as accidents, natural disasters, or financial losses. This expertise will enhance your ability to develop risk management strategies and make informed decisions under uncertainty.

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Healthcare Consultant

Healthcare Consultants provide advice and guidance to healthcare organizations on improving operations and patient outcomes. The Survival Analysis course in R will equip you with valuable skills for analyzing healthcare data, identifying inefficiencies, and evaluating the effectiveness of interventions. This knowledge will enhance your ability to develop evidence-based recommendations and contribute to the optimization of healthcare systems.

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Biomedical Engineer

Biomedical Engineers design and develop medical devices and technologies. The Survival Analysis course in R offers valuable insights into statistical methods used in biomedical engineering, including techniques for analyzing clinical data, evaluating device performance, and predicting patient outcomes. This knowledge will enhance your understanding of biomedical data analysis and support your contributions to the development of innovative medical technologies.

See salaries and explore the career path for Biomedical Engineer

Medical Writer

Medical Writers create and edit medical content, such as scientific articles, patient education materials, and regulatory documents. The Survival Analysis course in R may be helpful for Medical Writers who need to understand and explain statistical methods used in medical research, particularly in the context of clinical trials and patient outcomes analysis. This knowledge will enhance your ability to accurately convey complex medical information to diverse audiences.

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Market Researcher

Market Researchers conduct research to understand consumer behavior and market trends. The Survival Analysis course in R may be helpful for Market Researchers who need to analyze data related to customer retention, product usage, or brand loyalty. By learning survival analysis techniques, you can identify factors that influence customer behavior over time and develop effective marketing strategies accordingly.

See salaries and explore the career path for Market Researcher