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Mechanistic Analysis of Challenge–Response Experiments
Author(s) -
Shotwell M. S.,
Drake K. J.,
Sidorov V. Y.,
Wikswo J. P.
Publication year - 2013
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12066
Subject(s) - context (archaeology) , heteroscedasticity , computer science , nonlinear system , inference , statistical inference , monte carlo method , econometrics , machine learning , mathematics , statistics , artificial intelligence , physics , paleontology , quantum mechanics , biology
Summary We present an application of mechanistic modeling and nonlinear longitudinal regression in the context of biomedical response‐to‐challenge experiments, a field where these methods are underutilized. In this type of experiment, a system is studied by imposing an experimental challenge, and then observing its response. The combination of mechanistic modeling and nonlinear longitudinal regression has brought new insight, and revealed an unexpected opportunity for optimal design. Specifically, the mechanistic aspect of our approach enables the optimal design of experimental challenge characteristics (e.g., intensity, duration). This article lays some groundwork for this approach. We consider a series of experiments wherein an isolated rabbit heart is challenged with intermittent anoxia. The heart responds to the challenge onset, and recovers when the challenge ends. The mean response is modeled by a system of differential equations that describe a candidate mechanism for cardiac response to anoxia challenge. The cardiac system behaves more variably when challenged than when at rest. Hence, observations arising from this experiment exhibit complex heteroscedasticity and sharp changes in central tendency. We present evidence that an asymptotic statistical inference strategy may fail to adequately account for statistical uncertainty. Two alternative methods are critiqued qualitatively (i.e., for utility in the current context), and quantitatively using an innovative Monte‐Carlo method. We conclude with a discussion of the exciting opportunities in optimal design of response‐to‐challenge experiments.