Survival Analysis in R - 2-Day Live Training Course

Date:

07/11/2022 - 08/11/2022

Organised by:

Mind Project Ltd

Presenter:

Simon Walkowiak MSc, MBPsS

Level:

Intermediate (some prior knowledge)

Contact:

Simon Walkowiak
Phone: 02033223786
Email: info@mindproject.co.uk

video conference logo

Venue: Online

Description:

1. Course description.

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.

More details available at https://www.mindproject.io/product/survival-analysis-in-r-2-day-live-training-course-november-2022/

Who is this course for? This course will be especially useful to ambitious MSc students, PhD-level and post-doc researchers, data scientists and business analysts interested in learning and applying an array of industry-standard and cutting-edge survival analysis methods including machine learning and AI approaches to predict censored data.

 

2. Course programme.

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, 10:00-16:30 London (UK) time

Deadline for registrations: Friday, 4th of November 2022 @ 17:00 London (UK) time

 

During this training 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 (including Tobit and 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.

 

3. 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 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.

 

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/survival-analysis-in-r-2-day-live-training-course-november-2022/

Cost:

By 17th of October 2022 (Early Bird offer): £450 (normally £600) per person for the whole course (regular fee). £330 (normally £450) per person for the whole course applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee). Additional discounts available for multiple bookings and groups.

Website and registration:

Region:

Greater London

Keywords:

Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Log-linear regression, Event History Analysis, Hazard analysis, Survival analysis, Time Series Analysis, Forecasting, Data Mining, Neural networks, Machine learning, Quantitative Software, R

Related publications and presentations:

Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Log-linear regression
Event History Analysis
Hazard analysis
Survival analysis
Time Series Analysis
Forecasting
Data Mining
Neural networks
Machine learning
Quantitative Software
R

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