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Related Concept Videos

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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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Classification of Illness01:17

Classification of Illness

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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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Related Experiment Videos

R and S-PLUS produced different classification trees for predicting patient mortality.

Peter C Austin1

  • 1Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Department of Public Health Sciences, University of Toronto, Canada; Department of Health Management, Policy and Evaluation, University of Toronto, Canada.

Journal of Clinical Epidemiology
|July 16, 2008
PubMed
Summary

Classification and regression trees are useful in biomedical research. This study found that while R produced a more parsimonious tree, S-PLUS yielded more accurate and discriminatory classification trees for predicting mortality.

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Computational Biology
  • Data Science in Healthcare

Background:

  • Classification and regression trees are increasingly utilized in biomedical research.
  • R and S-PLUS are statistical programming languages with similar syntax and functionality, both capable of fitting classification and regression trees.

Purpose of the Study:

  • To compare classification trees generated using R with those generated using S-PLUS.
  • To evaluate the differences in tree structure and predictive accuracy between R and S-PLUS.

Main Methods:

  • Comparison of classification trees for mortality prediction in 9,484 acute myocardial infarction patients.
  • Utilized repeated split-sample derivation to assess predictive accuracy.

Main Results:

  • The classification tree grown using R was more parsimonious than the S-PLUS tree.
  • The pruned R tree was equivalent to a significantly pruned S-PLUS tree.
  • Classification trees from S-PLUS demonstrated superior discrimination and accuracy compared to R trees.

Conclusions:

  • R and S-PLUS can produce different classification trees even with identical data.
  • S-PLUS provided more accurate predictive models for mortality in this acute myocardial infarction cohort.