scholarly journals An update on modeling dose–response relationships: Accounting for correlated data structure and heterogeneous error variance in linear and nonlinear mixed models1,2

2016 ◽  
Vol 94 (5) ◽  
pp. 1940-1950 ◽  
Author(s):  
M. A. D. Gonçalves ◽  
N. M. Bello ◽  
S. S. Dritz ◽  
M. D. Tokach ◽  
J. M. DeRouchey ◽  
...  
Keyword(s):  
Data Structure ◽  
Dose Response ◽  
Error Variance ◽  
Correlated Data ◽  
Linear And Nonlinear

scholarly journals Agricultural decision-making under uncertainty: a loss function on the kriging variance from soil properties predicted by infrared and X-ray fluorescence spectroscopy

2021 ◽  
Author(s):  
Timo Breure ◽  
Stephan M. Haefele ◽  
Richard Webster ◽  
Jacqueline A. Hannam ◽  
Ronald Corstanje ◽  
...  
Keyword(s):  
Fluorescence Spectroscopy ◽  
Loss Function ◽  
Dose Response ◽  
Response Curve ◽  
Error Variance ◽  
Error Distribution ◽  
Expected Loss ◽  
Fertilizer Rate ◽  
Wet Chemistry ◽  
X Ray

<p>Obtaining detailed soil information at a scale suitable for variable rate application (VRA) of fertilizer requires intense sampling. Within this context, traditional wet chemistry analysis of the soil is too costly to provide sufficient detail on the spatial variation of soils within fields. Infrared and X-ray fluorescence spectroscopy of soil is much cheaper and might provide sufficient data to map the concentrations affordably. Estimates of the concentrations from spectroscopy are subject to error, however, and these errors in turn carry with them costs if they lead to under- or over-applications of fertilizer and resultant loss of potential crop yields or environmental pollution. Given a loss function and an error distribution for the estimates of the concentrations, it is possible to minimize the expected loss from VRA fertilizer management.</p><p>Within this study, we have estimated the variation of available P and K within horticultural fields and the effect of errors on the expected loss. The topsoil (0-25 cm) of four fields was sampled at numerous points and analysed by infrared and X-ray spectroscopy to provide estimates of available P and K. . The spatial variation in the estimates were modelled as mixtures of fixed and random effects by residual maximum likelihood (REML), with the spectroscopic model error accounted for in the parameter estimates. These models where then used to map the P and K content by universal kriging together with their kriging variances. Loss functions were computed based on the error distribution and a dose response curve from the literature. We then computed the total loss (compared to perfect information) for each field under VRA of fertilizer and a blanket fertilizer scheme based on the wet chemistry data.</p><p>Underestimation of the error variance in four out of eight linear mixed models underlines the importance of error propagation to estimate the short-scale spatial variance of the soil property correctly. As expected, the optimum fertilizer rate when accounting for uncertainty tends towards over-application. The asymmetry of the loss function described that underestimation generally leads to a higher loss compared to overestimation of soil P and K. The effect of error variance on the expected loss was further found to be dependent on the range of the kriging predictions relative to the parameterization of the dose response curve. There was a financial incentive for VRA of P fertilizer but not for K. Additionally there is an environmental incentive for VRA of P because much less fertilizer would be applied compared to a blanket fertilizer rate based on wet chemistry data.</p>

scholarly journals Estimates of Cancer Potency of 2,3,7,8-Tetrachlorodibenzo(p)dioxin Using Linear and Nonlinear Dose-Response Modeling and Toxicokinetics

2009 ◽  
Vol 112 (2) ◽  
pp. 490-506 ◽  
Author(s):  
Ted Simon ◽  
Lesa L. Aylward ◽  
Christopher R. Kirman ◽  
J. Craig Rowlands ◽  
Robert A. Budinsky
Keyword(s):  
Dose Response ◽  
Linear And Nonlinear ◽  
Tetrachlorodibenzo P Dioxin ◽  
Response Modeling

scholarly journals Meta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and Software

2011 ◽  
Vol 175 (1) ◽  
pp. 66-73 ◽  
Author(s):  
Nicola Orsini ◽  
Ruifeng Li ◽  
Alicja Wolk ◽  
Polyna Khudyakov ◽  
Donna Spiegelman
Keyword(s):  
Dose Response ◽  
Meta Analysis ◽  
Linear And Nonlinear

Dose-Response Relationships to Pancreozymin in Normal Subjects and Patients with Chronic Pancreatitis

1963 ◽  
Vol 45 (2) ◽  
pp. 209-214 ◽  
Author(s):  
Donald H. Hanscom ◽  
Armand Littman ◽  
Jack V. Pinto
Keyword(s):  
Chronic Pancreatitis ◽  
Dose Response ◽  
Normal Subjects

291 Normal myocardial dose-response to dobutamine as assessed by tissue Doppler stress echocardiography

1999 ◽  
Vol 1 ◽  
pp. S47-S47
Author(s):  
N PAYNE ◽  
R GROCOTTMASON ◽  
A IONESCU ◽  
B SJOBERG ◽  
I SANDBLOM ◽  
...  
Keyword(s):  
Dose Response ◽  
Stress Echocardiography ◽  
Tissue Doppler
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