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Diagnostics for repeated measurements in nonlinear mixed effects models

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  • Jungwon Mun
  • Minkyung Oh

Abstract

This paper introduces a new non-deletion method which identifies discordant subjects in nonlinear mixed effects models under a self-modeling framework. The new method decomposes the population level residuals into two parts and suggests two-dimensional plots to identify discordant subjects. An observation-wise investigation for alleged discordant subjects is also presented. The performances of the new methods are illustrated with simulation data and two real data examples. The new methods successfully identify the intended or important discordant subjects and observations. In a comparison with the local influence method, the new method reaches a consistent conclusion in a simpler and more efficient manner.

Suggested Citation

  • Jungwon Mun & Minkyung Oh, 2020. "Diagnostics for repeated measurements in nonlinear mixed effects models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(20), pages 5045-5059, October.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:20:p:5045-5059
    DOI: 10.1080/03610926.2019.1612916
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