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Multi-level models for longitudinal growth norms

H Pan1, H Goldstein

  • 1Institute of Education, London, U.K.

Statistics in Medicine
|January 9, 1998
PubMed
Summary
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This study introduces multi-level models to estimate longitudinal growth norms. These models transform measurements for accurate modeling, enabling derived growth standards for various intervals and functions.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Growth Modeling

Background:

  • Accurate estimation of growth norms is crucial for monitoring child development.
  • Existing methods may lack flexibility for diverse growth patterns or intervals.

Purpose of the Study:

  • To present multi-level models for estimating conditional and unconditional longitudinal growth norms.
  • To provide a flexible framework for deriving growth standards applicable to various time points and functions.

Main Methods:

  • Utilizing multi-level modeling with a two-level random coefficient structure.
  • Transforming original growth measurements to achieve Normality before modeling.
  • Applying the models to illustrate derivation of growth norms using height and weight data.

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Main Results:

  • The proposed multi-level models effectively estimate longitudinal growth norms.
  • The procedure allows for the derivation of growth norms across any specified time interval.
  • Normality transformation enhances the robustness of the modeling approach.

Conclusions:

  • Multi-level models offer a powerful and flexible approach for establishing longitudinal growth norms.
  • The method is applicable to various growth metrics, including height and weight.
  • This framework supports precise assessment of individual growth trajectories relative to population standards.