TY - JOUR
T1 - On the estimation of hypoxic ventilatory response
AU - Engeman, Richard M.
AU - Sherrill, Duane L.
AU - Swanson, George D.
PY - 1985/12
Y1 - 1985/12
N2 - The shape parameter, A, has been used to assess hypoxic sensitivity (ventilatory response, V ̇E, to lowered alveolar oxygen tension, PAO2) in the model of the form V ̇E = V ̇EO + A (PAO2 - C) where C is held constant at 32. In this paper we examine the consequence of holding C constant versus estimating both A and C from the data. Using computer-simulated data from 59 subjects whose A and C values had been previously determined, we indicate that when the actual C value is substantially different from 32, the model with C = 32 does not fit the data. In this case, the estimated A value with C = 32 is highly dependent upon the lowest value of PAO2 obtained in the experimental protocol and can be markedly different from the actual A value. In contrast, when C is also estimated from the subject's data the model fits the data and the estimate of A is unbiased but the precision may be diminished when the actual value of C is low. To improve this precision, we propose a Bayesian estimation scheme that adds a "soft" constraint on C.
AB - The shape parameter, A, has been used to assess hypoxic sensitivity (ventilatory response, V ̇E, to lowered alveolar oxygen tension, PAO2) in the model of the form V ̇E = V ̇EO + A (PAO2 - C) where C is held constant at 32. In this paper we examine the consequence of holding C constant versus estimating both A and C from the data. Using computer-simulated data from 59 subjects whose A and C values had been previously determined, we indicate that when the actual C value is substantially different from 32, the model with C = 32 does not fit the data. In this case, the estimated A value with C = 32 is highly dependent upon the lowest value of PAO2 obtained in the experimental protocol and can be markedly different from the actual A value. In contrast, when C is also estimated from the subject's data the model fits the data and the estimate of A is unbiased but the precision may be diminished when the actual value of C is low. To improve this precision, we propose a Bayesian estimation scheme that adds a "soft" constraint on C.
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U2 - 10.1016/0010-4809(85)90030-8
DO - 10.1016/0010-4809(85)90030-8
M3 - Article
C2 - 4075789
AN - SCOPUS:0022352568
SN - 1532-0464
VL - 18
SP - 553
EP - 562
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 6
ER -