A PC program for diagnosing abnormal growth, growth velocity and acceleration from longitudinal observations

Area of Science:

• Biometrics
• Longitudinal Data Analysis
• Growth Modeling

Background:

• Longitudinal data analysis is crucial for understanding growth patterns.
• Accurate characterization of normal growth, velocity, and acceleration is essential for individual assessment.
• Existing methods may have limitations with missing data or distributional assumptions.

Purpose of the Study:

• To describe and make available a PC program implementing Zerbe's procedure for diagnosing normality based on longitudinal data.
• To provide a tool for characterizing normal growth, velocity, and acceleration.
• To enable the assessment of whether an individual conforms to normal growth parameters.

Main Methods:

• Implementation of Zerbe's procedure for longitudinal data analysis using GAUSS386i.
• The program accommodates missing data points.
• No assumption of normal distribution is required for the monitored variable.

Main Results:

• The described PC program effectively implements Zerbe's method for longitudinal growth analysis.
• The method is robust to missing data, as demonstrated with an illustrative dataset.
• The program allows for the characterization of normal growth, velocity, and acceleration.

Conclusions:

• The developed PC program offers a valuable tool for the statistical analysis of longitudinal growth data.
• It provides a flexible approach to normality assessment in growth studies, accommodating common data challenges.
• The availability of the program facilitates its application in research and clinical settings.