Bibliographies thématiques / Nonlinear dose-response regressions

Sommaire

Articles de revues
Thèses
Chapitres de livres

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

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 19 juillet 2025

Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres

Choisissez une source :

Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Nonlinear dose-response regressions ».

À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.

Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.

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.

Texte intégral

Résumé :

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 the desired confidence levels. Sometimes they could underestimate the true nominal level and are thus liberal. Hence one needs to be cautious in using the usual linearized standard errors of MLE and the associated confidence intervals.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 total PA and moderate-tovigorous PA (MVPA) at each time. Outcomes data came from medical records.Results:The dose-response curves between PA or MVPA and cesarean risk at 17 to 22 weeks followed an inverse J-shape, but at 27 to 30 weeks the curves reversed and were J-shaped. However, only (total) PA at 27 to 30 weeks was strongly associated with cesarean risk; this association was attenuated when women reporting large volumes of PA (> 97.5 percentile) were excluded.Conclusion:We did not find evidence of an association between physical activity and cesarean birth. We did, however, find evidence that associations between PA and risk of cesarean may be nonlinear and dependent on gestational age at time of exposure, limiting the accuracy of analyses that collapse maternal PA into categories.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 was to determine the maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D) of PEN-866 given weekly (3 out of 4 weeks in a 28-day cycle). Results: 30 pts were treated in 8 cohorts (range 30-360 mg flat dosing or 150–200 mg/m2 BSA-based dosing). As of 9Jan20, the total median/mean exposure was 7.05/12.4 weeks. No dose limiting toxicities (DLTs) occurred in the first 4 cohorts (30-240 mg; 14 pts). In cohort 5 (360 mg), 1 of 3 pts had a DLT of grade (G) 3 transient diarrhea, and 2 other pts had G3 uncomplicated transient neutropenia. A change to BSA-based dosing was instituted for cohort 6 (175 mg/m2), on which no DLTs were observed, although 1 pt experienced G3 uncomplicated transient neutropenia. At 200 mg/m2, 2 of 5 pts experienced DLTs (G5 dehydration, G3 fatigue). The MTD and RP2D were determined to be 175 mg/m2. The most frequent (≥20% pts) related adverse events were nausea (50%), fatigue (43%), diarrhea (40%), vomiting (27%), and anemia (23%). PK was nonlinear. Plasma exposures increased greater than dose proportionally. Median t1/2 ~7 h. Cleaved SN38 never exceeded 3% of PEN-866 plasma AUC at all dose levels. Tissue PK confirmed tumor accumulation and retention of both the conjugate and released payload. As of 9Jan20, 26 pts were evaluable for response. 11 pts had stable disease at 8 weeks, of which 7 lasted 12–58 weeks. One pt with anal squamous cell carcinoma achieved a confirmed partial response. Decreased target lesion size was observed in 6 additional pts. Conclusions: PEN-866 was well tolerated and demonstrated preliminary evidence of antitumor activity. PEN-866 will be evaluated in Phase 2a expansion cohorts enrolling multiple solid tumors (NCT03221400). Clinical trial information: NCT03221400 .

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 nonlinear regression models. Procedures for a valid comparison of effective doses between different allelopathic interactions are presented for both, inhibitory and stimulatory effects. The dose-response applications measure and compare the responses produced by pure allelochemicals [scopoletin (7-hydroxy-6-methoxy-2 H-1-benzopyran-2-one); DIBOA (2,4-dihydroxy-2 H-1,4-benzoxaxin-3(4 H)-one); BOA (benzoxazolin-2(3 H)-one); MBOA (6-methoxy-benzoxazolin-2(3 H)-one)], involved in allelopathy of grain crops, to demonstrate how some general principles of dose responses also relate to allelopathy. Hereupon, dose-response applications with living donor plants demonstrate the validity of these principles for density-dependent phytotoxicity of allelochemicals produced and released by living plants ( Avena sativa L., Secale cereale L., Triticum L. spp.), and reveal the use of such experiments for initial considerations about basic principles of allelopathy. Results confirm that nonlinearity applies to allelopathy, and the study of allelopathic effects in dose-response experiments allows for new and challenging insights into allelopathic interactions.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 (NHANES) and from the UK Biobank with baseline information provided from 2006 to 2010. Included participants had complete data on serum albumin concentration, RDW, and cause of death. The NHANES data were linked to the National Death Index records through December 31, 2019. For the UK Biobank, dates and causes of death were obtained from the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland) to November 30, 2022.Main Outcomes and MeasuresPotential associations between RAR and the risk of all-cause and cause-specific mortality were evaluated using Cox proportional hazards regression models. Restricted cubic spline regressions were applied to estimate possible nonlinear associations.ResultsIn NHANES, 50 622 participants 18 years of age or older years were included (mean [SD] age, 48.6 [18.7] years; 26 136 [51.6%] female), and their mean (SD) RAR was 3.15 (0.51). In the UK Biobank, 418 950 participants 37 years of age or older (mean [SD], 56.6 [8.1] years; 225 038 [53.7%] female) were included, and their mean RAR (SD) was 2.99 (0.31). The NHANES documented 7590 deaths over a median (IQR) follow-up of 9.4 (5.1-14.2) years, and the UK Biobank documented 36 793 deaths over a median (IQR) follow-up of 13.8 (13.0-14.5) years. According to the multivariate analysis, elevated RAR was significantly associated with greater risk of all-cause mortality (NHANES: hazard ratio [HR], 1.83 [95% CI, 1.76-1.90]; UK Biobank: HR, 2.08 [95% CI, 2.03-2.13]), as well as mortality due to malignant neoplasm (NHANES: HR, 1.89 [95% CI, 1.73-2.07]; UK Biobank: HR, 1.93 [95% CI, 1.86-2.00]), heart disease (NHANES: HR, 1.88 [95% CI, 1.74-2.03]; UK Biobank: HR, 2.42 [95% CI, 2.29-2.57]), cerebrovascular disease (NHANES: HR, 1.35 [95% CI, 1.07-1.69]; UK Biobank: HR, 2.15 [95% CI, 1.91-2.42]), respiratory disease (NHANES: HR, 1.99 [95% CI, 1.68-2.35]; UK Biobank: HR, 2.96 [95% CI, 2.78-3.15]), diabetes (NHANES: HR, 1.55 [95% CI, 1.27-1.90]; UK Biobank: HR, 2.83 [95% CI, 2.35-3.40]), and other causes of mortality (NHANES: HR, 1.97 [95% CI, 1.86-2.08]; UK Biobank: HR, 2.40 [95% CI, 2.30-2.50]) in both cohorts. Additionally, a nonlinear association was observed between RAR levels and all-cause mortality in both cohorts.Conclusions and RelevanceIn this cohort study, a higher baseline RAR was associated with an increased risk of all-cause and cause-specific mortality in the general population. These findings suggest that RAR may be a simple, reliable, and inexpensive indicator for identifying individuals at high risk of mortality in clinical practice.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 with nonlinear regression analysis. In terms of their ability to provide accurate estimates of ED50 and maximal response, none approached the accuracy and precision of nonlinear regression. This technique is as easily performed as the classic methods and additionally provides an opportunity for rigorous statistical analysis of data. We present a method of determining the significance of differences found in the ED50 and maximal response under different experimental conditions. The method is versatile and applicable to a variety of different physiological and pharmacological dose-response curves.

Styles APA, Harvard, Vancouver, ISO, etc.

Plus de sources

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.

Texte intégral

Résumé :

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 depending on any killing threshold. First, a model-free version of this titer is presented and shown to be more precise than the traditional threshold-based titers when using simulated and experimental group B Streptococcus (GBS) OPKA experimental data. Second, a model-based threshold-free titer is introduced to automatically take into account the potential saturation of the OPKA killing curve. The posterior distributions of threshold-based and threshold-free titers is derived for each analysed sample using importance sampling embedded within a Markov chain Monte Carlo sampler of the coefficients of a 4PL logistic dose-response model. The posterior precision of threshold-free titers is again shown to be higher than that of threshold-based titers. The biological interpretability and operational characteristics demonstrated here indicate that threshold-free titers can substantially improve the routine analysis of OPKA experimental and clinical data.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 (i.e. occur in observational studies) or Berkson type (i.e. occur in designed experiments). This thesis addresses the problem of the Berkson EIV and their effect on the statistical analysis of data fitted using linear and nonlinear models. In particular, the case when the errors are dependent and have heterogeneous variance is studied. Both analytical and empirical tools have been used to develop new approaches for dealing with this type of errors. Two different scenarios are considered: mixture experiments where the model to be estimated is linear in the parameters and the EIV are correlated; and bioassay dose-response studies where the model to be estimated is nonlinear. EIV following Gaussian distribution, as well as the much less investigated non-Gaussian distribution are examined. When the errors occur in mixture experiments both analytical and empirical results showed that the naive analysis produces biased and inefficient estimators for the model parameters. The magnitude of the bias depends on the variances of the EIV for the mixture components, the model and its parameters. First and second Scheffé polynomials are used to fit the response. To adjust for the EIV, four different approaches of corrections are proposed. The statistical properties of the estimators are investigated, and compared with the naive analysis estimators. Analytical and empirical weighted regression calibration methods are found to give the most accurate and efficient results. The approaches require the error variance to be known prior to the analysis. The robustness of the adjusted approaches for misspecified variance was also examined. Different error scenarios of EIV in the settings of concentrations in bioassay dose-response studies are studied (i.e. dependent and independent errors). The scenarios are motivated by real-life examples. Comparisons between the effects of the errors are illustrated using the 4-prameter Hill model. The results show that when the errors are non-Gaussian, the nonlinear least squares approach produces biased and inefficient estimators. An extension of the well-known simulation-extrapolation (SIMEX) method is developed for the case when the EIV lead to biased model parameters estimators, and is called Berkson simulation-extrapolation (BSIMEX). BSIMEX requires the error variance to be known. The robustness of the adjusted approach for misspecified variance is examined. Moreover, it is shown that BSIMEX performs better than the regression calibration methods when the EIV are dependent, while the regression calibration methods are preferable when the EIV are independent.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 is the Richards model shown both as an equation and as computer code.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 drug. You don’t know the actual concentration of drug at its site of action-you only know the total dose that you administered. However, the term “dose-response curve” is also used more loosely to describe in vitro experiments where you apply known concentrations of drugs. The term “concentration-response curve” is therefore a more precise label for the results of these types of experiments. The term “dose-response curve” is occasionally used even more loosely to refer to experiments where you vary levels of some other variable, such as temperature or voltage.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 high efficacy. Since the two dose-response curves are identical, there is no data analysis technique that can tell them apart. You need to analyze afamily of curves, not an individual curve, to determine the affinity and efficacy. The rest of this chapter explains how.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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 subtracted), it can make sense for you to constrain the bottom of the dose-response curve to equal zero. In some situations, it can make sense to constrain the Hill slope to equal a standard value of 1.0.

Styles APA, Harvard, Vancouver, ISO, etc.

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.

Texte intégral

Résumé :

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, while sharing the best-fit value of the logEC50. Fitting this model requires a program that can do global fits with shared parameters.

Styles APA, Harvard, Vancouver, ISO, etc.

Nous offrons des réductions sur tous les plans premium pour les auteurs dont les œuvres sont incluses dans des sélections littéraires thématiques. Contactez-nous pour obtenir un code promo unique!