Infant growth modelling using a shape invariant model with random effects

Area of Science:

• Biostatistics
• Pediatric Growth Modeling
• Non-parametric Statistics

Background:

• Traditional parametric infant growth models often fit poorly, especially in the first year.
• Existing models struggle with individual variations and covariate effects.

Purpose of the Study:

• To develop and apply a flexible non-parametric infant growth model.
• To accurately model infant weight trajectories from birth to two years.
• To investigate the impact of covariates, such as breastfeeding, on infant growth.

Main Methods:

• Utilized a shape invariant model (SIM) with regression splines for a non-parametric approach.
• Employed nonlinear mixed-effects modeling to account for inter-subject variability.
• Developed methods for incorporating time-independent and time-dependent covariates.

Main Results:

• The novel SIM-based model demonstrated superior fitting of infant weight data compared to traditional methods.
• The model successfully identified significant effects of covariates on infant growth trajectories.
• Breastfeeding was analyzed as a covariate influencing infant weight gain.

Conclusions:

• The developed non-parametric SIM offers a robust and adaptable framework for infant growth modeling.
• This approach enhances the understanding of factors influencing infant development.
• The model provides a valuable tool for analyzing longitudinal growth data in pediatric studies.