Statistical Analysis of Longitudinal Data with a Case Study

Abstract

Preterm birth is the leading cause of neonatal mortality and long-term morbidity. Neonatologists can adjust nutrition to preterm neonates to control their weight gain so that the possibility of long-term morbidity can be minimized. This optimization of growth trajectories of preterm infants can be achieved by studying a cohort of selected healthy preterm infants with weights observed during day 1 to day 21. However, missing values in such a data poses a big challenge in this case. In fact, missing data is a common problem faced by most applied researchers. Most statistical softwares deal with missing data by simply deleting subjects with missing items. Analyses carried out on such incomplete data result in biased estimates of the parameters of interest and consequently lead to misleading or invalid inference. Even though many statistical methods may provide robust analysis, it will be better to handle missing data by imputing them with plausible values and then carry out a suitable analysis on the full data. In this thesis, several imputation methods are first introduced and discussed. Once the data get completed by the use of any of these methods, the growth trajectories for this cohort of preterm infants can be presented in the form of percentile growth curves. These growth trajectories can now serve as references for the population of preterm babies. To find out the explicit growth rate, we are interested in establishing predictive models for weights at days 7, 14 and 21. I have used both univariate and multivariate linear models on the completed data. The resulting predictive models can then be used to calculate the target weight at days 7, 14 and 21 for any other infant given the information at birth. Then, neonatologists can adjust the amount of nutrition given in order to preterm infants to control their growth so that they will not grow too fast or too slow, thus avoiding later-life complications.