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survival data

Parametric or Semi-Parametric Models in Survival Analysis?

by guest Leave a Comment

It was Casey Stengel who offered the sage advice, “If you come to a fork in the road, take it.”

When you need to fit a regression model to survival data, you have to take a fork in the road. One road asks you to make a distributional assumption about your data and the other does not. [Read more…] about Parametric or Semi-Parametric Models in Survival Analysis?

Tagged With: cox, distributions, exponential, gamma, hazard function, lognormal, parametric models, regression models, semi-parametric, survival data, Weibull

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Member Training: An Introduction to Kaplan-Meier Curves

by guest Leave a Comment

Survival data models provide interpretation of data representing the time until an event occurs. In many situations, the event is death, but it can also represent the time to other bad events such as cancer relapse or failure of a medical device. It can also be used to denote time to positive events such as pregnancy. Often patients are lost to follow-up prior to death, but you can still use the information about them while they were in your study to better estimate the survival probability over time.

This is done using the Kaplan-Meier curve, an approach developed by [Read more…] about Member Training: An Introduction to Kaplan-Meier Curves

Tagged With: curve, estimate, events, Kaplan-Meier curve, model, survival data, survival percentiles, survival probability

Related Posts

  • Member Training: Latent Growth Curve Models
  • The Proportional Hazard Assumption in Cox Regression
  • Member Training: Cox Regression
  • Member Training: Discrete Time Event History Analysis

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