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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Related Experiment Videos

Q-LEARNING WITH CENSORED DATA.

Yair Goldberg1, Michael R Kosorok

  • 1Department of Biostatistics, The University of North Carolina At Chapel Hill, Chapel Hill, NC 27599, U.S.A.

Annals of Statistics
|July 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new Q-learning algorithm for multistage decision problems with censored survival data. It enables flexible stages and personalized treatment strategies, improving outcomes in critical diseases.

Related Experiment Videos

Area of Science:

  • Decision Sciences
  • Machine Learning
  • Biostatistics

Background:

  • Censored survival data is common in clinical trials.
  • Multistage decision problems require adaptive strategies.
  • Personalized medicine necessitates individualized treatment regimens.

Purpose of the Study:

  • Develop a Q-learning algorithm for multistage decision problems with censored data.
  • Allow for a flexible number of decision stages.
  • Improve individualized treatment strategies for complex diseases.

Main Methods:

  • Novel Q-learning algorithm adapted for censored survival times.
  • Finite sample bounds on generalization error.
  • Simulation of a multistage clinical trial.

Main Results:

  • The algorithm effectively handles censored data in multistage settings.
  • Convergence of learned policies to optimal policy under specific conditions.
  • Successful application in simulated personalized treatment regimens.

Conclusions:

  • The proposed censored-Q-learning methodology is effective for flexible-stage decision problems.
  • This approach has significant implications for personalized medicine trials.
  • Enables more precise individualized treatment strategies in oncology and other critical diseases.