Survival Analysis in R – 2-Day Live Training Course – November 2022
£450.00 – £750.00
Course dates: 7th-8th of November 2022, 10:00-16:00 London (UK) time
Course type: 2-day instructor-led live online course with certification
Recommended time commitment: 24 hours including self-study
Deadline for registrations: Friday, 4th of November 2022 @ 17:00 London (UK) time
Book your place on this course by 17th of October 2022 to be eligible for the Early Bird discount.
Survival analysis is a collection of statistical and forecasting methods commonly used to predict the survival time and to estimate the probability of an event within a specific period of time. Its techniques are often applied in healthcare (e.g. to estimate the time of hospital readmission/discharge, probability of death during the specific time of the follow-up period etc.), bioinformatics (e.g. cancer survival based on gene expression data), business (e.g. customer churn prediction, customer lifetime value etc.), higher education (e.g. student retention forecasts), and manufacturing (e.g. predicting the time of failure for specific devices or components).
This 2-day instructor-led live training course has been designed to provide you with a deep understanding of statistical models (i.e. non-parametric, semi-parametric and parametric) as well as machine learning-based survival analysis methods and their implementations in the R programming language. The 1st day of the course comprehensively covers industry-standard survival analysis approaches such as Kaplan-Meier, Cox regression and more complex techniques e.g. time-dependent Cox models and regularised regressions. The 2nd day of the course is dedicated to machine learning and AI time-to-event methods applicable to high-dimensional censored data e.g. survival trees, Bayesian methods and more black-box approaches such as Support Vector Machines (SVMs) and tree-based ensembles (i.e. supermodels).
The presented tutorials will utilise datasets from a variety of fields: social sciences, biomedical sciences, economics and business. The course will implement custom-made R code as well as methods and functions available in selected R packages e.g. survival, BMA, fastcox, randomForestSRC, mboost, AER and many others.
Who is this course for?
This course will be especially useful to data scientists, business/risk analysts, econometricians, bioinformaticians as well as ambitious MSc students, PhD-level and post-doc researchers interested in learning and applying an array of industry-standard and cutting-edge survival analysis methods including machine learning and AI approaches to model and predict a number of problems based on censored data e.g. time to event, health/diagnostic outcomes e.g. survival forecasts, customer churn/retention, academic dropout rates, time-to-failure and similar.
During this course, you will:
- Understand the mathematical underpinnings and statistical assumptions of the hazard function typically used in survival analysis and computational complexity of the methods presented during the course,
- Estimate the hazard function with the Kaplan-Meier and Nelson-Aalen non-parametric methods, the semi-parametric Cox regression approach and parametric censored linear regression (e.g. Tobit or Buckley-James regressions) as well as the accelerated failure time (AFT) models,
- Implement more complex statistical survival methods e.g. time-dependent (i.e. with time-dependent covariates) and penalised Cox regression models including Lasso, Ridge and Elastic Net regularisation techniques,
- Apply cutting-edge machine learning and AI algorithms tailored to handle censored data e.g. survival trees with different spitting criteria, Bayesian methods (e.g. Naive Bayes and Bayesian networks), and the variants of Support Vector Machines (SVMs) and tree-based ensemble methods (e.g. bagging survival trees and random survival forests) specifically designed for survival analysis purposes,
- Discuss current developments in survival analysis methods e.g. active and transfer learning, modern implementations of neural networks of different topologies,
- Evaluate the survival prediction performance with metrics designed for censored data e.g. the concordance index (C-index), mean absolute error (MAE), and Brier score,
- Visualise survival analysis results using R language e.g. with the Kaplan-Meier curve and other approaches.
What is included in the course fee?
At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:
- 2 days of remotely delivered live tutorials, lectures and presentations with our expert tutor – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
- 1-year access to the course area on Mind Project Learning Platform with the recorded videos of the training sessions and additional course resources (i.e. research articles, book excerpts, and additional online resources),
- R code scripts, datasets and copy of presented materials used during the course,
- 2-week post-course follow-up period during which you will be able to work on homework exercises and submit them for our feedback – the feedback will be provided either via email or on Teams (i.e. as a 15-minute 1-2-1 session with our tutor),
- support throughout the course and the 2-week post-course follow-up period – feel free to ask us questions about the material presented at the training during this period.
This is a 2-day instructor-led online training course with a week-long follow up period. The course will run from 10:00 in the morning to ~16:30 each day and will include a 45-minute break for lunch between morning and afternoon sessions and two 10-minute coffee/tea breaks. During the course, you will consolidate your skills during short coding exercises and instructor-moderated discussions. Additionally, you will be able to test the implementations on a selected dataset. Following the course, you will be able to submit your solutions to the homework exercises and you will receive feedback from the tutor.
This training course is instructor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor after the course if you have any questions related to the material presented during the course.
The course will be recorded – you will have access to the video recordings of the course webinars and additional resources such as datasets, R code, academic papers related to the topics of the workshop, and supplementary exercises via Mind Project Learning Platform.
Course dates: Monday-Tuesday, 7th-8th of November 2022, 10:00-16:00 London (UK) time
Deadline for registrations: Friday, 4th of November 2022 @ 17:00 London (UK) time
Course pre-requisites and further instructions
- We recommend that you have the most recent version of R and R Studio software installed on your PC (any operating system). R is a free and open-source environment and you can download it directly from https://cloud.r-project.org/ website. RStudio Desktop (also free) is available at https://rstudio.com/products/rstudio/download/. Please contact us should you have any questions or issues with the installation process. A list of R packages to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other Joining Instructions.
- We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. Additionally, you should have some experience in using R language for statistical analysis (e.g. you should be comfortable implementing R methods/functions for inferential hypothesis testing and standard R libraries for data wrangling such as the tidyverse family of packages e.g. dplyr, ggplot2 etc.). We suggest that the course is preceded with our “Applied Data Science with R” open-to-public training course.
- Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
- By enrolling on one of our online training courses you agree to the Training Terms and Conditions. Please read the Training Terms and Conditions before purchasing this course. Please note we reserve the right to cancel the course in case the number of registered attendees on the course is less than 5 individuals one week before its scheduled start date.
Your course instructor
Your instructor for this course will be Simon Walkowiak. Simon is the director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 180 in-house or open-to-public statistical training courses (in R, Python, SQL, Java and Scala) in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse, ING etc.), research and academia (GSMA, CERN, University of Cambridge, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), insurance (Liberty IT), transport (Steer Group) and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).