Abstract
During heart defibrillator implantation, a physician fibrillates the patient’s heart several times at different test strengths to estimate the effective strength necessary for defibrillation. One strategy is to implant at the strength that de-fibrillates 95% of the time (ED95). Efficient choice and use of the test strengths in this estimation problem is crucial, as each additional observation increases the risk of serious injury or death. Such choice can be formalized as finding an optimal design in, say, a logistic regression problem with interest in estimating the ED95. In practice, important features specific to this problem are the very small sample sizes; the asymmetry of the loss function; and the fact that the prior distribution arises as the distribution for the next draw of patient-specific parameters in a hierarchical model.
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Clyde, M., Müller, P., Parmigiani, G. (1995). Optimal Design for Heart Defibrillators. In: Gatsonis, C., Hodges, J.S., Kass, R.E., Singpurwalla, N.D. (eds) Case Studies in Bayesian Statistics, Volume II. Lecture Notes in Statistics, vol 105. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2546-1_7
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DOI: https://doi.org/10.1007/978-1-4612-2546-1_7
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