Design and analysis for longitudinal animal studies with high-dimensional outcomes

Sound experimental design, followed up by rigorous statistical analysis of the results, is of great importance in studies involving animals, where for ethical and economic reasons, we aim to reduce the number of animals used while making sure that the sample size is sufficient to gain the required knowledge. The potential for reducing the number of animals needed by employing efficient experimental designs is huge. When animals or litters are measured repeatedly over time, we can use longitudinal design and analysis methods to gain statistical power. However, existing methods assume that each sample at each time provides only one, or at most a few, measured outcomes (Diggle, Heagerty, Liang and Zeger, 2002).

Meanwhile, modern high-throughput biotechnologies deliver very high-dimensional outcome data from a single sample. While most research acknowledges the dependence amongst different dimensions of the data (e.g. linkage disequilibrium in the genome, spatial correlation in brain function imaging), there is a need for study design and analysis methods that bridge the gap between traditional longitudinal studies and the high-dimensional world of biomedicine.

This project is concerned with developing a statistical model for the design and analysis of high-dimensional longitudinal studies, based on generalized linear mixed models and Gaussian processes. We will apply this method to existing mouse functional brain imaging data from the lab of our collaborator Dr Neil Dawson. The aim will be to gain new scientific insights into developmental changes in mouse brain function, and to demonstrate the effectiveness of the high-dimensional longitudinal method for increasing the statistical power and reducing the number of mice needed..