Introduction

Welcome to the Survival analysis of Left-Truncated and Right-Censored data ReCoDe Project

This project is designed to guide you through the process of performing survival analysis using large cohort data. Survival analysis, also known as time-to-event analysis, is a statistical method used to quantify the risk and timing of events, such as death, disease onset, or other significant occurrences. This type of analysis is particularly powerful in medical and epidemiological research, where understanding the duration until an event occurs can provide critical insights into patient outcomes and treatment efficacy.

It is structured into a series of sections, which will introduce you to the fundamental concepts, techniques, and applications of survival analysis. The aim is for you to learn how to manage and analyse large data sets, apply appropriate statistical models, interpret results, and draw meaningful conclusions about survival probabilities and hazard functions.

Project Layout

flowchart LR
  A(Data Curation) --> B(Cox Proportional Regression Model)
  B --> C(Univariate Analysis)
  B --> D(Multivariate Analysis)
  C --> E[Kaplan -Meier Curve]
  C --> F[Log-Rank Test]
  D --> G[Hazard Ratio Table]
  D --> H[Forest Plot]
  
  F --> I{Interpretation of results}
  E --> I
  G --> I
  H --> I

Learning outcomes

At the end of this project you should be able to:

To understand the different types of censoring and how to curate your data
Conduct univariate and multivariate survival analysis using R
Graphically present the findings of a survival analysis
Interpret the results from a survival analysis

Pre-requisites

It would be very useful to take some courses offered by the Graduate School at Imperial College London, either as an introduction or a refresher:

Research Computing & Data Science Skills Courses

Introductory and teaching materials

The following lecture aims to give you an overview of what survival analysis is. It also includes sections explaining the different types of censoring and truncation that need to be considered when conducting a survival analysis.

There is an example using the UK Biobank data which gives you an insight into how to interprete the results and top tips to consider.

Survival analysis of Left-Truncated and Right-Censored Data using the UK Biobank

Other resources

NTUN Lecture: Introduction to R

Survival Analysis Textbook

Advanced Survival Analysis Blog