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Nonlinear mixed-effects (NLME) models can be enhanced to analyze complex repeated measures data. This study demonstrates using SAS to model serial correlation and variance heterogeneity in residual structures for more realistic intraindividual variation analysis.

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Nonlinear mixed-effects (NLME) models are widely used for repeated measures data analysis.
  • Current NLME implementations often assume independent residuals with homogeneous variances, which may not reflect reality.
  • This assumption is often due to software limitations rather than biological plausibility.

Purpose of the Study:

  • To demonstrate how SAS can be programmed to model complex residual structures in NLME.
  • To address limitations in handling serial correlation and variance heterogeneity within NLME models.
  • To provide a more realistic approach to analyzing intraindividual variation in repeated measures data.

Main Methods:

  • Utilizing the programmatic environment within SAS for advanced statistical modeling.
  • Implementing custom residual structures to capture serial correlation.
  • Modeling variance heterogeneity to account for non-constant variances over time or conditions.
  • Applying these methods to an empirical dataset for illustration.

Main Results:

  • Successful implementation of NLME models with specified serial correlation and variance heterogeneity in SAS.
  • Demonstration of how to overcome software limitations for more flexible residual modeling.
  • The empirical example highlights the practical application and benefits of the proposed approach.

Conclusions:

  • SAS provides a flexible platform for advanced NLME modeling, including complex residual structures.
  • Accounting for serial correlation and variance heterogeneity leads to more accurate modeling of intraindividual variation.
  • This approach enhances the analysis of continuous repeated measures data, offering more realistic insights into individual change over time.