Introduction: In biomedical systems repeated measurements are often collected, thus presenting a statistical challenge due to high temporal correlation. This research\ud investigates the potential utility of two distinct statistical methodologies in their application.\ud \ud Application: Two clinically diverse biomedical systems, linked by the common methodological interest of assessing control and performance, are considered. (i) An application to renal anaemia aims to investigate the stability of haemoglobin levels, measured monthly for 13 months within 151 patients, with the ultimate goal of improving\ud patient control; (ii) the second an application concerns cerebral autoregulation (a stable cerebral blood flow over a range of arterial blood pressure), to maintain patient safety during a surgical procedure to prevent stroke. Repeated measurements of cerebral blood flow and arterial blood pressure were collected on 36 patients, yielding a total of 4519 cerebral blood flow and 4574 arterial blood pressure measurements (note that the number of observations vary between patients).\ud \ud Statistical methodology: Functional data analysis and multilevel modelling are utilised in the investigation of these two biomedical systems. Functional data analysis considers observations as a function rather than a highly correlated sequence of measurements. Multilevel modelling assumes that measurements are clustered and that within clusters, measurements are scattered about a trend in an uncorrelated manner.\ud \ud Results: Assessment of control within the renal anaemia system and knowledge of the relationship within the cerebral autoregulation system, has been achieved through\ud the successful application of functional data analysis. Loess curves were used as means of exploring the cerebral blood flow – arterial blood pressure relationship in the cerebral autoregulation application. B-splines and phase plots were used to explore haemoglobin control in the renal anaemia system. Further, multilevel modellingincorporating autoregressive correlation structures appropriately models the dependency amongst model residuals due to temporal correlation. Both functional data analysis and multilevelmodelling have demonstrated their utility in the application to model control in biomedical systems. \ud \ud Conclusions: The novel application of these statistical methodologies has successfully provided contemporary insight into these biomedical systems and shows strong prospects for further applications
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