Longitudinal models of iron status in a population-based cohort of mothers and children in southwest England
Longitudinal data requires special statistical methods because the observations on one subject tend to be correlated. (Although subjects can usually be assumed to be independent). When subjects are individually observed at varying sets of times with or without missing data, as is the case of ALSPAC data during pregnancy, then the resulting data is referred to as unbalanced data. This can cause further complications for the analysis. The aim of this thesis is to contribute to longitudinal research of this topic by using mixed-effects models, which provide a powerful and flexible tool for the analysis of balanced and unbalanced data. Although progress has been made in the study reported in this thesis, further extensions are required. As the longitudinal data typically need some structured covariance models, the overall findings indicate that when the number of occasions is large with some missing values, the use of polynomial function is inadequate to describe the model. This study highlights an approach that applies cubic spline in longitudinal modelling, including an emphasis on the use of graphical representation for exploratory analysis and the assessment of model fit. Cubic splines provide a flexible tool for longitudinal data. The main objective of this study is to investigate a methodology to incorporate cubic spline with linear mixed models in modelling longitudinal data with number of time points and missing values.