The Complete Survival Analysis Course In R
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 46m | Size: 1.14 GB
A complete course of Survival Analysis from basic concepts to real life projects
What you'll learn
Understand core concepts in survival analysis (e.g, censoring, survival function, hazard function).
Understand the fundamentals of survival analysis methods (e.g., Kaplan-Meier analysis, Log-rank test, Cox regression).
Proficiently perform survival analyses as well as build Cox regression model using R.
Check the assumptions for survival analyses and handle violations.
Requirements
No prior experience in survival analysis is required, but a basic understanding of R and statistics will help you get the most from the course.
Description
AIMS OF THE COURSEAre you ready to explore the world of time-to-event data, particularly survival analysis and master the essential techniques used in medical research, public health, and social sciences? This course is designed to equip you with both the theoretical understanding and practical skills needed to confidently perform survival analysis using R.WHO THIS COURSE IS FOR?Whether you're a student, researcher, or data professional, this course will walk you through key survival analysis concepts-from censoring and survival functions to advanced models like the Cox proportional hazards regression. You'll not only learn the theory but also apply it to real-world projects and datasets.OUTCOMESBy the end of the course, you will be able to:Grasp core concepts in survival analysis such as censoring, hazard functions, and survival curves.Understand the fundamentals of survival analysis methods including Kaplan-Meier analysis, Log-rank test, and Cox regression.Proficiently perform survival analyses as well as build Cox regression model using RCreate summary tables and charts (i.e., KM curve, baseline hazards and hazards rates)Evaluate and test model assumptions, and handle violations.ABOUT MYSELFLam Nguyen is a dedicated educator and experienced data analysis with a strong background in biotechnology, biology, and statistical analysis and modeling. He holds a PhD and MSc from Flinders University, with additional qualifications from Mahidol University, Osaka University, and Hanoi University of Science (VNU). Lam has served as a lecturer, research associate, and consultant, working across academic and applied research projects in Australia and Asia.With a passion for coding, data analysis, and lifelong learning, Lam brings both academic rigor and hands-on insight to this course, ensuring learners gain practical, applicable skills.
Who this course is for
Graduate students in biostatistics, epidemiology and public health
Researchers working with time-to-event data
Data scientists and analysts in healthcare and other fields
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