Abstract
In observational clinical studies, subjects’ health status is empirically assessed according to research protocols that prescribe aspects to investigate and methods for investigation. Commonly to many fields of research, these studies are frequently affected by incompleteness of information, a problem that, if not duly handled, may seriously invalidate conclusions drawn from investigations. Regarding cardiovascular risk assessment, coronary risk factors (e.g. high blood pressure) and proxies of neurovegetative domain (e.g. heart rate variability) are individually evaluated through direct measurements taken in laboratory. A major cause of missingness can be ascribed to the fact that overall sets of collected data typically derive from aggregation of a multitude of sub-studies, undertaken at different times and under slightly different protocols that might not involve the same variables. Data on certain variables can thus be missing if such variables were not included in all protocols. This issue is addressed by referring to a clinical case study concerning the role of Autonomic Nervous System in the evaluation of subjects’ health status.
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Solaro, N., Lucini, D., Pagani, M. (2017). Handling Missing Data in Observational Clinical Studies Concerning Cardiovascular Risk: An Insight into Critical Aspects. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_14
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