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Related Experiment Videos

Likelihood-based diagnostics for influential individuals in non-linear mixed effects model selection.

S Sadray1, E N Jonsson, M O Karlsson

  • 1Department of Pharmacy, Faculty of Pharmacy, Uppsala University, Sweden.

Pharmaceutical Research
|September 1, 1999
PubMed
Summary
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New diagnostics help identify individuals influencing nonlinear mixed effects model selection. These methods improve model choice by pinpointing data points that disproportionately affect results.

Area of Science:

  • Statistical modeling
  • Pharmacometrics
  • Biostatistics

Background:

  • Non-linear mixed effects (NLME) models are crucial in analyzing complex biological and pharmacological data.
  • Model selection in NLME can be heavily influenced by data from individual subjects.
  • Existing diagnostic tools are not optimized for identifying individuals impacting model selection decisions.

Purpose of the Study:

  • To introduce and evaluate two novel likelihood-based diagnostic methods.
  • These methods specifically aim to identify individuals that influence the selection between competing NLME models.

Main Methods:

  • Method 1: Jackknifing individual-level data and refitting the NLME model.
  • Method 2: Calculating individual contributions to objective function values under each model.

Related Experiment Videos

  • Application of both methods to a real-world dataset for model selection.

    • Main Results:

    • High agreement was observed between the two proposed diagnostic methods.
    • Individuals identified by discrepancies between methods often indicated poor model fit for that subject.
    • Both diagnostics successfully pinpointed subjects that influenced the choice between competing NLME models.

    Conclusions:

    • Two objective, quantitative, and specific methods for identifying influential individuals in NLME model selection are presented.
    • One method offers an advantage by not requiring additional model fitting, enhancing efficiency.
    • These diagnostics provide valuable tools for robust model building in NLME analysis.