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Statistical Considerations and Techniques for Understanding Physiological Data, Modeling, and Treatments

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Cardiovascular Engineering

Abstract

Comparing models with data always forces us to deal with uncertainty. This uncertainty may take many different forms and involve multiple scales of resolution in the model and in the experiment. In this paper, we discuss issues surrounding the development of deterministic dynamic models of mean behavior and the associated statistical models of the difference between model and experiment. We touch on a variety of topics, including basic exploratory data analysis, confidence bounds and model reduction hypothesis tests. Tools ranging from nonlinear regression to time series to Bayesian decision theory are presented.

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Acknowledgments

We are grateful to the American Institute for Mathematics for hosting the Workshop on Short-Term Cardiovascular-Respiratory Control Mechanisms, at which this material was presented. We are especially grateful to Professors Jerry Batzel, Ary Goldberger, Franz Kappel, Johnny Otteson, Mette Olufsen, Vladimir Protopopescu, and Hien Tran for many stimulating and illuminating discussions during the workshop.

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Authors and Affiliations

  1. Department of Mathematics, Loyola Marymount University, Los Angeles, USA

    Ben G. Fitzpatrick

  2. Tempest Technologies, Los Angeles, USA

    Ben G. Fitzpatrick

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  1. Ben G. Fitzpatrick
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Correspondence to Ben G. Fitzpatrick.

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Fitzpatrick, B.G. Statistical Considerations and Techniques for Understanding Physiological Data, Modeling, and Treatments. Cardiovasc Eng 8, 135–143 (2008). https://doi.org/10.1007/s10558-007-9052-6

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  • DOI: https://doi.org/10.1007/s10558-007-9052-6

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