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JAMAevidence JAMA Guide to Statistics and Methods

Time-to-Event Analysis With Dr Tolles

Mar 3, 2023
In this podcast, Dr Juliana Tolles discusses time-to-event analysis in clinical research. She explores the concept of survival curves, Kaplan-Meier curves, and statistical tests to compare survival rates. She also explains the differences between the log-rank test and the Cox proportional hazards model. Additionally, she analyzes the proportional hazard assumption and provides an example study comparing major adverse cardiac events.
11:19

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Time-to-event analysis compares rates of positive or adverse events, even if events are not observed during follow-up.
  • Survival curves, Kaplan-Meier plots, and statistical tests like log-rank test and Cox proportional hazards model are used to compare groups and quantify risk factors in survival analysis.

Deep dives

Time to Event Analysis: An Introduction

In this podcast episode, the hosts discuss time to event analysis, also known as survival analysis. The analysis compares the rate at which patients experience different outcomes, such as positive or adverse events. One challenge in analyzing this type of data is that patients may be followed for different periods of time or may be lost to follow-up. Survival data can arise in various forms in medicine, reflecting the time until a patient experiences an event, whether positive or adverse. The key point is that even if an event is not observed during the follow-up period, the fact that the event didn't occur provides valuable information.

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