Improved confidence interval for average annual percent change in trend analysis

Hyune Ju Kim, Jun Luo, Huann Sheng Chen, Don Green, Dennis Buckman, Jeffrey Byrne, Eric J. Feuer

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t-distribution instead of the standard normal distribution. The second method is based on the empirical distribution of the residuals and the resampling using a uniform random sample, and its satisfactory performance is indicated by a simulation study.

Original languageEnglish (US)
Pages (from-to)3059-3074
Number of pages16
JournalStatistics in Medicine
Volume36
Issue number19
DOIs
StatePublished - Aug 30 2017

Keywords

  • confidence interval
  • empirical distribution
  • joinpoint
  • resampling
  • segmented line regression

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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    Kim, H. J., Luo, J., Chen, H. S., Green, D., Buckman, D., Byrne, J., & Feuer, E. J. (2017). Improved confidence interval for average annual percent change in trend analysis. Statistics in Medicine, 36(19), 3059-3074. https://doi.org/10.1002/sim.7344