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

Content Provider	Europe PMC
Author	Kim, Hyune-Ju Chen, Huann-Sheng Midthune, Douglas Wheeler, Bill Buckman, Dennis W. Green, Donald Byrne, Jeffrey Luo, Jun Feuer, Eric J.
Copyright Year	2022
Description	Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC ( BIC3). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial R2 to determine the weight between BIC and BIC3. The proposed method is computationally much more efficient than the permutation test procedure that has been the default method of Joinpoint software developed for cancer trend analysis, and its satisfactory performance is observed in our simulation study. Simulations indicate that the proposed method performs well in keeping the probability of correct selection at least as large as that of BIC3, whose performance is comparable to that of the permutation test procedure, and improves BIC3 when it performs worse than BIC. The proposed method is applied to the U.S. prostate cancer incidence and mortality rates.
Abstract	Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC (BIC3). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial R2 to determine the weight between BIC and BIC3. The proposed method is computationally much more efficient than the permutation test procedure that has been the default method of Joinpoint software developed for cancer trend analysis, and its satisfactory performance is observed in our simulation study. Simulations indicate that the proposed method performs well in keeping the probability of correct selection at least as large as that of BIC3, whose performance is comparable to that of the permutation test procedure, and improves BIC3 when it performs worse than BIC. The proposed method is applied to the U.S. prostate cancer incidence and mortality rates.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10291945&blobtype=pdf
Page Count	22
ISSN	02664763
Volume Number	50
DOI	10.1080/02664763.2022.2063265
PubMed Central reference number	PMC10291945
Issue Number	9
PubMed reference number	37378270
Journal	Journal of Applied Statistics [J Appl Stat]
e-ISSN	13600532
Language	English
Publisher	Taylor & Francis
Publisher Date	2022-04-18
Access Restriction	Open
Rights License	© 2022 Informa UK Limited, trading as Taylor & Francis Group
Subject Keyword	Bayesian information criterion change-point probability of correct selection segmented line regression weighted
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty