Statistical modeling for growth data in linear mixed models – Implications derived from an example of a population comparison of Golden Hamsters
Abstract. Using statistical modeling to determine the structure of expectation and covariance employed during analysis is a common feature of analytical research. This paper describes the necessary methodology for, and illustrates those techniques that are of special importance in, practical modeling and evaluation scenarios (likelihood ratio test, analytical criteria, residual analysis). Our approach is demonstrated upon a population comparison, taken on various measurement dates, that focuses on a wild population and a laboratory population of Golden Hamsters. The selected example is particularly suited due to the fact that – aside from the actual growth function of interest – additional fixed (e.g. effect of different mating periods, litter size) and random factors (e.g. maternal environment, repeated performances per animal) must be considered. The modeling shows significant efficiency regarding the improvement of the analytical criteria. The recommended evaluation model leads to a very close match of the observed ordinary least square residuals and of the variance and covariance functions, respectively, that have been derived from the estimated covariance structure.