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Littérature scientifique sur le sujet « Nonlinear dose-response regressions »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Nonlinear dose-response regressions"

Peddada, Shyamal D., and Joseph K. Haseman. "Analysis of Nonlinear Regression Models: A Cautionary Note." Dose-Response 3, no. 3 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.03.005.

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Regression models are routinely used in many applied sciences for describing the relationship between a response variable and an independent variable. Statistical inferences on the regression parameters are often performed using the maximum likelihood estimators (MLE). In the case of nonlinear models the standard errors of MLE are often obtained by linearizing the nonlinear function around the true parameter and by appealing to large sample theory. In this article we demonstrate, through computer simulations, that the resulting asymptotic Wald confidence intervals cannot be trusted to achieve

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Bovbjerg, Marit L., Anna Maria Siega-Riz, Kelly R. Evenson, and William Goodnight. "Exposure Analysis Methods Impact Associations Between Maternal Physical Activity and Cesarean Delivery." Journal of Physical Activity and Health 12, no. 1 (2015): 37–47. http://dx.doi.org/10.1123/jpah.2012-0498.

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Background:Previous studies report conflicting results regarding a possible association between maternal physical activity (PA) and cesarean delivery.Methods:Seven-day PA recalls were collected by telephone from pregnant women (n = 1205) from North Carolina, without prior cesarean, during 2 time windows: 17 to 22 weeks and 27 to 30 weeks completed gestation. PA was treated as a continuous, nonlinear variable in binomial regressions (log-link function); models controlled for primiparity, maternal contraindications to exercise, preeclampsia, pregravid BMI, and percent poverty. We examined both t

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Falchook, Gerald Steven, Johanna C. Bendell, Susanna Varkey Ulahannan, et al. "Pen-866, a miniature drug conjugate of a heat shock protein 90 (HSP90) ligand linked to SN38 for patients with advanced solid malignancies: Phase I and expansion cohort results." Journal of Clinical Oncology 38, no. 15_suppl (2020): 3515. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.3515.

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3515 Background: PEN-866 is a miniature drug conjugate which links a HSP90 binding small molecule to a SN-38 cytotoxic payload. HSP90 is highly expressed in advanced malignancies. PEN-866 targets and binds to activated tumor HSP90 protein, releases its cytotoxic payload, and results in complete tumor regressions in multiple xenograft models. This first-in-human study assessed safety, tolerability, pharmacokinetics (PK), and preliminary efficacy of PEN-866. Methods: Patients (pts) with progressive, advanced solid malignancies were enrolled in escalating cohorts of 2-9 pts. The primary objective

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Bárdossy, András, István Bogárdi, and Lucien Duckstein. "Fuzzy nonlinear regression analysis of dose-response relationships." European Journal of Operational Research 66, no. 1 (1993): 36–51. http://dx.doi.org/10.1016/0377-2217(93)90204-z.

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NIELSEN, OLE K., CHRISTIAN RITZ, and JENS C. STREIBIG. "Nonlinear Mixed-Model Regression to Analyze Herbicide Dose–Response Relationships1." Weed Technology 18, no. 1 (2004): 30–37. http://dx.doi.org/10.1614/wt-03-070r1.

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Belz, Regina G., Karl Hurle, and Stephen O. Duke. "Dose-Response—A Challenge for Allelopathy?" Nonlinearity in Biology, Toxicology, Medicine 3, no. 2 (2005): nonlin.003.02.0. http://dx.doi.org/10.2201/nonlin.003.02.002.

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The response of an organism to a chemical depends, among other things, on the dose. Nonlinear dose-response relationships occur across a broad range of research fields, and are a well established tool to describe the basic mechanisms of phytotoxicity. The responses of plants to allelochemicals as biosynthesized phytotoxins, relate as well to nonlinearity and, thus, allelopathic effects can be adequately quantified by nonlinear mathematical modeling. The current paper applies the concept of nonlinearity to assorted aspects of allelopathy within several bioassays and reveals their analysis by no

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Hao, Meng, Shuai Jiang, Jingdong Tang, et al. "Ratio of Red Blood Cell Distribution Width to Albumin Level and Risk of Mortality." JAMA Network Open 7, no. 5 (2024): e2413213. http://dx.doi.org/10.1001/jamanetworkopen.2024.13213.

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ImportanceThe ratio of red blood cell distribution width (RDW) to albumin concentration (RAR) has emerged as a reliable prognostic marker for mortality in patients with various diseases. However, whether RAR is associated with mortality in the general population remains unknown.ObjectivesTo explore whether RAR is associated with all-cause and cause-specific mortality and to elucidate their dose-response association.Design, Setting, and ParticipantsThis population-based prospective cohort study used data from participants in the 1998-2018 US National Health and Nutrition Examination Survey (NHA

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Gutjahr, Georg, and Björn Bornkamp. "Likelihood ratio tests for a dose-response effect using multiple nonlinear regression models." Biometrics 73, no. 1 (2016): 197–205. http://dx.doi.org/10.1111/biom.12563.

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Sakiyama, Yojiro, Katsuyo Ohashi, and Yukio Takahashi. "Application of nonlinear regression model to sigmoid dose-response relationship in pharmacological studies." Folia Pharmacologica Japonica 132, no. 4 (2008): 199–206. http://dx.doi.org/10.1254/fpj.132.199.

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Meddings, J. B., R. B. Scott, and G. H. Fick. "Analysis and comparison of sigmoidal curves: application to dose-response data." American Journal of Physiology-Gastrointestinal and Liver Physiology 257, no. 6 (1989): G982—G989. http://dx.doi.org/10.1152/ajpgi.1989.257.6.g982.

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A number of physiological or pharmacological studies generate sigmoidal dose-response curves. Ideally, data analysis should provide numerical solutions for curve parameters. In addition, for curves obtained under different experimental conditions, testing for significant differences should be easily performed. We have reviewed the literature over the past 3 years in six journals publishing papers in the field of gastrointestinal physiology and established the curve analysis technique used in each. Using simulated experimental data of known error structure, we have compared these techniques wit

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Thèses sur le sujet "Nonlinear dose-response regressions"

MORASCHINI, LUCA. "Likelihood free and likelihood based approaches to modeling and analysis of functional antibody titers with applications to group B Streptococcus vaccine development." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/76794.

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Opsonophagocytic killing assays (OPKA) are routinely used for the quantification of bactericidal antibodies against Gram-positive bacteria in clinical trial samples. The OPKA readout, the titer, is traditionally estimated using non-linear dose-response regressions as the highest serum dilution yielding a predefined threshold level of bacterial killing. Therefore, these titers depend on a specific killing threshold value and on a specific dose-response model. This thesis describes a novel OPKA titer definition, the threshold free titer, which preserves biological interpretability whilst not dep

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Althubaiti, Alaa Mohammed A. "Dependent Berkson errors in linear and nonlinear models." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/dependent-berkson-errors-in-linear-and-nonlinear-models(d56c5e58-bf97-4b47-b8ce-588f970dc45f).html.

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Often predictor variables in regression models are measured with errors. This is known as an errors-in-variables (EIV) problem. The statistical analysis of the data ignoring the EIV is called naive analysis. As a result, the variance of the errors is underestimated. This affects any statistical inference that may subsequently be made about the model parameter estimates or the response prediction. In some cases (e.g. quadratic polynomial models) the parameter estimates and the model prediction is biased. The errors can occur in different ways. These errors are mainly classified into classical (

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Chapitres de livres sur le sujet "Nonlinear dose-response regressions"

Altenburg, Hans-Peter. "On Robust Nonlinear Regression Methods Estimating Dose Response Relationships." In Compstat. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-52463-9_26.

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Cox, Louis Anthony. "Modeling Nonlinear Dose-Response Functions: Regression, Simulation, and Causal Networks." In International Series in Operations Research & Management Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57358-4_2.

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Wagenpfeil, Stefan, Uwe Treiber, and Antonie Lehmer. "A MATLAB-Based Software Tool for Changepoint Detection and Nonlinear Regression in Dose-Response Relationships." In Medical Data Analysis. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39949-6_23.

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Motulsky, Harvey, and Arthur Christopoulos. "Complex dose-response curves." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0044.

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Abstract The standard (Hill) sigmoidal dose-response model is based on the assumption that the log(dose) vs. response curve is symmetrical around its midpoint. But some dose-response curves are not symmetrical. In a recent study, Van der Graaf and Schoemaker (J. Pharmacol. Toxicol. Meth., 41: 107-115, 1999) showed that the application of the Hill equation to asymmetric dose-response data can lead to quite erroneous estimates of drug potency (EC50). They suggested an alternative model, known as the Richards equation, which could provide a more adequate fit to asymmetric dose-response data. Here

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Motulsky, Harvey, and Arthur Christopoulos. "Introduction to dose-response curves." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0041.

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Abstract Dose-response curves can be used to plot the results of many kinds of experiments. The X axis plots concentration of a drug or hormone. The Y axis plots response, which could be almost any measure of biological function. For example, the response might be enzyme activity, accumulation of an intracellular second messenger, membrane potential, secretion of a hormone, change in heart rate, or contraction of a muscle. The term “dose” is often used loosely. In its strictest sense, the term only applies to experiments performed with animals or people, where you administer various doses of d

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Motulsky, Harvey, and Arthur Christopoulos. "Fitting data with nonlinear regression." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0001.

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Abstract Nonlinear regression is used to fit data to a model that defines Yas a function ofX. Y must be a variable like weight, enzyme activity, blood pressure or temperature. Some books refer to these kinds of variables, which are measured on a continuous scale, as “interval” variables. For this example, nonlinear regression will be used to quantify the potency of the drug by determining the dose of drug that causes a response halfway between the minimum and maximum responses. We’ll do this by fitting a model to the data.

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Motulsky, Harvey, and Arthur Christopoulos. "The operational model of agonist action." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0042.

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Abstract Fitting a standard sigmoidal (logistic) equation to a dose-response curve to determine EC50 (and perhaps slope factor) doesn’t tell you everything you want to know about an agonist. The problem is that the EC50 is determined by two properties of the agonist: A single dose-response experiment cannot untangle affinity from efficacy. Two very different drugs could have identical dose-response curves, with the same EC50 values and maximal responses (in the same tissue). One drug binds tightly with high affinity but has low efficacy, while the other binds with low affinity but has very hig

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Motulsky, Harvey, and Arthur Christopoulos. "Dose-response curves in the presence of antagonists." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0043.

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Abstract The term antagonist refers to any drug that will block, or partially block, a response. When investigating an antagonist, the first thing to check is whether the antagonism is surmountable by increasing the concentration of agonist. The next thing to ask is whether the antagonism is reversible. After washing away antagonist, does agonist regain response? If an antagonist is surmountable and reversible, it is likely to be competitive (see next paragraph). Investigations of antagonists that are not surmountable or reversible are beyond the scope of this manual.

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Motulsky, Harvey, and Arthur Christopoulos. "Constraining and sharing parameters." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0046.

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Abstract The Constraints tab of the nonlinear regression dialog is very versatile. For each parameter, you can choose to fix it to a constant value, constrain it to a range of values, or share its value between data sets. In many cases, it makes sense to constrain one (or more) of the parameters to a constant value. For example, even though a dose-response curve is defined by four parameters (bottom, top, logEC5o and Hill slope), you don’t have to ask Prism to find best-fit values for all the parameters. If the data represent a “specific” signal (with any background or nonspecific signal subtr

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Motulsky, Harvey, and Arthur Christopoulos. "Using global fitting to test a treatment effect in one experiment." In Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195171792.003.0027.

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Abstract The logic underlying model comparisons described in the previous two chapters can also be extended to instances where you might want to compare one or more parameters of the same model applied to different data sets. Some examples are shown below. Below is a graph of a dose-response curve in control and treated conditions. We want to know if the treatment changes the EC50. Are the two best-fit logEC50 values statistically different? One hypothesis is that both data sets have the same EC50. We fit that model by doing a global fit of both data sets. We fit two dose-response curves, whil

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