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Predictive influence in the accelerated failure time model.

Edward J Bedrick1, Alex Exuzides, Wesley O Johnson

  • 1Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87131, USA. bedrick@stat.unm.edu

Biostatistics (Oxford, England)
|August 23, 2003
PubMed
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We developed new diagnostics to identify influential cases in survival analysis for improved future predictions. These tools help assess how removing data points affects survival curve and median failure time predictions.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Prediction is a crucial inferential goal in survival analysis.
  • Identifying influential observations is key to ensuring prediction quality.

Purpose of the Study:

  • To develop case deletion diagnostics for prediction in accelerated failure time models.
  • To assess the impact of individual data points on predictive inferences.

Main Methods:

  • Utilized Kullback-Leibler divergence to measure discrepancies between full and case-deleted probability distributions.
  • Focused on the effect of case deletion on survival curves as predictive tools.
  • Developed diagnostics for assessing case deletion's impact on median time to failure inferences.

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Main Results:

  • Proposed novel diagnostics for evaluating case deletion influence on predictions.
  • Demonstrated the utility of these diagnostics in assessing survival curve and median time to failure predictions.
  • Compared proposed measures with the established Cook distance for estimative influence.

Conclusions:

  • The developed diagnostics are valuable for assessing the influence of observations on predictions in survival analysis.
  • These methods enhance the reliability of future predictions derived from survival models.
  • The diagnostics provide insights into data point influence for both predictive and estimative purposes.