|
Investigating repeated measurements of physical function across life is of importance for many health outcomes. However, no one study can be expected to represent the complete population even if started at birth.
Therefore, information from several cohort studies can be used to better understand these measurements and make inference over the complete age range. However, combining data from various cohort studies is challenging to ensure that the study variability is properly modelled.
The aim is to develop the appropriate models to investigate the shape of these measurements across life by combining several cohort studies together. Bayesian adaptive splines are used to create a smooth line across life whilst adjusting for study effect, individual effect and several risk factors.
|