Survival Analysis and Churn Prediction with R – 2-Day Tutor-Led Training Course

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.

 

2. Course structure and programme.

This is a 2-day instructor-led online training course with a two-week-long follow up period. The course will run from 10:00 in the morning to ~16:00 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 homework exercises and you will receive feedback from the tutor.

This training course is tutor-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, 11th-12th of December 2023, 10:00-16:00 London (UK) time

Deadline for registrations: Friday, 8th of December 2023 @ 17:00 London (UK) time

 

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,

• 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.

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk. Please visit the course website at Survival Analysis and Churn Prediction with R - 2-Day Tutor-Led Training Course - December 2023.

Cost:

By 24th of November 2023 (Early Bird offer): £630 (normally £750) - Commercial fee - individual customers representing commercial/business entities, £480 (normally £600) - NGO/Gov/Academic fee - applicable to representatives of registered charitable organisations, national health service employees (e.g. NHS in the UK), employed academic staff (e.g. research assistants, lecturers, post-docs and above), and employees of governmental departments (e.g. civil servants), £330 (normally £450) - Student fee - applicable to undergraduate and postgraduate students only (confirmation of student status required). Additional discounts available for multiple bookings and groups.

Website and registration:

Register for this course

Region:

Greater London

Keywords:

Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Event History Analysis, Hazard analysis, Survival analysis, Duration analysis, Time Series Analysis, Forecasting, Data Mining, Machine learning, R

Related publications and presentations from our eprints archive:

Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Event History Analysis
Hazard analysis
Survival analysis
Duration analysis
Time Series Analysis
Forecasting
Data Mining
Machine learning
R

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