2010
DOI: 10.1211/jpp.62.03.0008
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Nonlinear mixed effects models applied to cumulative concentration–response curves

Abstract: Using the method presently described, it is now possible to detect significant differences for each pharmacological parameter estimated in different situations, even for designs with small samples size (i.e. at least six complete curves).

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Cited by 16 publications
(6 citation statements)
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“…Linear Mixed effect (LME) model was used to analyze data from non-CCRCs (isolated heart) and CCRCs to insulin. Meanwhile, Non-linear Mixed Effect model (NLME) was used to assess data from CCRCs to Phe and Ach [ 45 ]. Contraction and relaxation were expressed as the percentage relaxation of the KCl- and Phe-induced precontraction, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Linear Mixed effect (LME) model was used to analyze data from non-CCRCs (isolated heart) and CCRCs to insulin. Meanwhile, Non-linear Mixed Effect model (NLME) was used to assess data from CCRCs to Phe and Ach [ 45 ]. Contraction and relaxation were expressed as the percentage relaxation of the KCl- and Phe-induced precontraction, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Results were expressed as a mean ± SE of n experiments, where n is the number of pig hearts (one PCA/pig heart). Different CCRCs were compared on R software (R Development Core Team 2007), using the linear mixed-effects model (Pinheiro and Bates 2000;Thorin et al 2010;Kabbesh et al 2012). The linear mixed-effects model must follow a normal distribution of residuals.…”
Section: Discussionmentioning
confidence: 99%
“…To examine the general null hypotheses that there were no group differences for firing frequency and burst activity, current–response curves for chronic alcohol drinking and alcohol naïve P rats were initially compared using nonlinear mixed effects models (Vonesh and Carter, ). These models allow accurate estimation of population parameters from limited sample sizes and correct for variance heterogeneity often accompanying cumulative dose–response data (Thorin et al., ). Current response curves from 0 to −14 nA were constructed for each group of rats and group‐by‐curve interactions were fitted using the Linear and Nonlinear Mixed Effects Models package for R (Pinheiro and Bates, ), treating individual neurons as a random effect to account for within‐group variability in neuronal responses.…”
Section: Methodsmentioning
confidence: 99%
“…For determining EC 50 values, the data were normalized. For this, baseline values were subtracted from the responses after each ejection current; current–responses were converted to percent maximum response and curves were constructed based on a nonlinear mixed effects model (Thorin et al., ). Group EC 50 and E max estimates were compared by examining 95% confidence intervals.…”
Section: Methodsmentioning
confidence: 99%