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Data-driven choice of a model selection method in joinpoint regression.

Hyune-Ju Kim1, Huann-Sheng Chen2, Douglas Midthune3

  • 1Department of Mathematics, Syracuse University, Syracuse, NY, USA.

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Summary
This summary is machine-generated.

A new method for selecting change points in segmented regression improves trend analysis efficiency. This approach uses modified Bayes Information Criteria (BIC) for accurate cancer trend analysis, outperforming existing methods.

Keywords:
Bayesian information criterionchange-pointprobability of correct selectionsegmented line regressionweighted

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Selecting the number of change points is crucial for segmented line regression and trend analysis.
  • Existing methods, like permutation tests in Joinpoint software, can be computationally intensive.

Purpose of the Study:

  • To propose a computationally efficient and accurate method for determining the number of change points in segmented regression.
  • To compare the performance of the new method against existing model selection procedures.

Main Methods:

  • Developed a novel model selection rule based on two Schwarz-type criteria: the classical Bayes Information Criterion (BIC) and a modified version with a harsher penalty.
  • The method utilizes partial to dynamically weigh BIC and the modified criterion based on effect sizes.
  • Evaluated the method's performance through simulations and compared it to the permutation test procedure.

Main Results:

  • The proposed method is significantly more computationally efficient than the permutation test procedure.
  • Simulations demonstrate that the new method maintains a high probability of correct selection, comparable to or better than existing approaches.
  • The method shows improved performance in scenarios where other methods falter.

Conclusions:

  • The proposed BIC-based method offers an efficient and effective alternative for change point selection in segmented regression.
  • This approach provides a valuable tool for trend analysis, particularly in fields like cancer epidemiology.
  • The method was successfully applied to analyze U.S. prostate cancer incidence and mortality rates.