DOI QR코드

DOI QR Code

Estimating dose-response curves using splines: a nonparametric Bayesian knot selection method

  • Lee, Jiwon (Department of Statistics, Kyungpook National University) ;
  • Kim, Yongku (Department of Statistics, Kyungpook National University) ;
  • Kim, Young Min (Department of Statistics, Kyungpook National University)
  • 투고 : 2021.06.21
  • 심사 : 2021.10.27
  • 발행 : 2022.05.31

초록

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF-2019R1F1A1061691) and research Grants of Korea Forest Service project (No.2019149A00-2123-0301)

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