LongCriLP: A test for bump hunting in longitudinal data

Jaroslaw Harezlak, Elena Naumova, Nan M. Laird

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

We propose an extension of the Harezlak and Heckman (J. Comput. Graph. Statist. 2001; 10(4): 713-729) test for detecting local extrema to the longitudinal data setting. We use penalized spline regression techniques (Statist. Sci. 1996; 11:89-102) to provide a computationally efficient method of testing for relatively large data sets. We estimate the p-values of our test, Long CriLP, with a smoothed bootstrap. Our simulation studies indicate that the test is generally conservative and has power exceeding 70 per cent at the α = 0.1 nominal level in most considered settings. Finally, we apply our testing procedure to the longitudinal measurements of body mass index of former prisoners of war in Vietnam and conclude that the mean population curve exhibits non-monotone behaviour.

Original languageEnglish (US)
Pages (from-to)1383-1397
Number of pages15
JournalStatistics in Medicine
Volume26
Issue number6
DOIs
StatePublished - Mar 15 2007
Externally publishedYes

Keywords

  • Body mass index
  • Bootstrap
  • Critical smoothing parameter
  • Longitudinal data
  • Penalized regression splines
  • Prisoners of war

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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