Methods for modelling repeated measures in a lifecourse framework

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Doctors are increasingly interested in long-term influences on health. For example, it is thought that people who were born small as babies, and grew faster during childhood, may be more likely to suffer cardiovascular disease in later life. Longitudinal studies ? studies in which individuals are followed over periods of many months or years ? are of great importance in understanding how aspects of people?s childhood lifestyle or environment influence their later health and wellbeing. When one measure (e.g. weight) is made several times on the same individual, the values at different ages are likely to be related. This raises difficult issues in the analysis of data from longitudinal studies, and failing to address these appropriately can lead to results that are biased (they differ from the results that would be observed if the analysis had been appropriate) or lead to inappropriate conclusions (the results of the analyses are interpreted incorrectly). Statistical methods that do address these issues have been proposed, and have the potential to decrease bias and increase ease of interpretation in analyses of longitudinal studies. However, these methods can be highly complex and difficult to apply. We will develop solutions to some of the problems with applying these methods, including developing strategies for modelling change over time (e.g. growth in childhood), and relating this change over time to later outcomes. We will incorporate our new methods into existing software, to maximise their future use, as well as publishing the results in scientific journals.