Comparisons of statistical models for growth curves from 90-day rat feeding studies

Abstract

The objective of this work was to compare several models of body weight data from 90-day rodent feeding trials. Polynomial and nonlinear functions relating time and weight were examined as were the use of Toeplitz error covariance structures and random coefficients. The models were evaluated by fitting them to five publicly available datasets from rat feeding studies. Model performance was assessed in terms of their ability to capture the complexity of the growth patterns, validity of necessary assumptions, and information criteria scores. The results demonstrated the importance of selecting a curve function that effectively reflects the mean response. Toeplitz error covariance structures resulted in superior model fit, while failing to address deviations from model assumptions. Models using the Richards function and random coefficients were generally superior to the other models evaluated and dramatically improved upon linear models with complex error structures.