Population pharmacokinetics of docetaxel during phase I studies using nonlinear mixed-effect modeling and nonparametric maximum-likelihood estimation

1995 ◽  
Vol 37 (1-2) ◽  
pp. 47-54 ◽  
Author(s):  
M. C. Launay-Iliadis ◽  
R. Bruno ◽  
V. Cosson ◽  
J. C. Vergniol ◽  
D. Oulid-Aissa ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Phase I ◽  
Likelihood Estimation ◽  
Nonparametric Maximum Likelihood ◽  
Mixed Effect ◽  
Phase I Studies ◽  
Nonlinear Mixed Effect ◽  
Nonlinear Mixed Effect Modeling ◽  
Mixed Effect Modeling

FORWARD APPORTIONMENT OF CENSORED COUNTS FOR DISCRETE NONPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATION OF FAILURE PROBABILITIES

2009 ◽  
Vol 16 (03) ◽  
pp. 213-234
Author(s):  
MINNIE H. PATEL ◽  
H.-S. JACOB TSAO
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Closed Form ◽  
Failure Probability ◽  
Likelihood Estimation ◽  
Lifetime Distribution ◽  
Nonparametric Maximum Likelihood ◽  
Kaplan Meier ◽  
Probability Estimates ◽  
Failure Probabilities

Empirical cumulative lifetime distribution function is often required for selecting lifetime distribution. When some test items are censored from testing before failure, this function needs to be estimated, often via the approach of discrete nonparametric maximum likelihood estimation (DN-MLE). In this approach, this empirical function is expressed as a discrete set of failure-probability estimates. Kaplan and Meier used this approach and obtained a product-limit estimate for the survivor function, in terms exclusively of the hazard probabilities, and the equivalent failure-probability estimates. They cleverly expressed the likelihood function as the product of terms each of which involves only one hazard probability ease of derivation, but the estimates for failure probabilities are complex functions of hazard probabilities. Because there are no closed-form expressions for the failure probabilities, the estimates have been calculated numerically. More importantly, it has been difficult to study the behavior of the failure probability estimates, e.g., the standard errors, particularly when the sample size is not very large. This paper first derives closed-form expressions for the failure probabilities. For the special case of no censoring, the DN-MLE estimates for the failure probabilities are in closed forms and have an obvious, intuitive interpretation. However, the Kaplan–Meier failure-probability estimates for cases involving censored data defy interpretation and intuition. This paper then develops a simple algorithm that not only produces these estimates but also provides a clear, intuitive justification for the estimates. We prove that the algorithm indeed produces the DN-MLE estimates and demonstrate numerically their equivalence to the Kaplan–Meier-based estimates. We also provide an alternative algorithm.

scholarly journals Pharmacokinetics of Efavirenz at a High Dose of 25 Milligrams per Kilogram per Day in Children 2 to 3 Years Old

2017 ◽  
Vol 61 (7) ◽  
Author(s):  
Claire Pressiat ◽  
Madeleine Amorissani-Folquet ◽  
Caroline Yonaba ◽  
Jean-Marc Treluyer ◽  
Désiré Lucien Dahourou ◽  
...  
Keyword(s):  
Antiretroviral Treatment ◽  
Recommended Dose ◽  
Pharmacokinetic Parameters ◽  
High Dose ◽  
Age Group ◽  
Mixed Effect ◽  
Sleeping Disorders ◽  
Nonlinear Mixed Effect ◽  
Nonlinear Mixed Effect Modeling ◽  
Mixed Effect Modeling

ABSTRACT The MONOD ANRS 12206 trial was designated to assess simplification of a successful lopinavir (LPV)-based antiretroviral treatment in HIV-infected children younger than 3 years of age using efavirenz (EFV; 25 mg/kg of body weight/day) to preserve the class of protease inhibitors for children in that age group. In this substudy, EFV concentrations were measured to check the consistency of an EFV dose of 25 mg/kg and to compare it with the 2016 FDA recommended dose. Fifty-two children underwent blood sampling for pharmacokinetic study at 6 months and 12 months after switching to EFV. We applied a Bayesian approach to derive EFV pharmacokinetic parameters using the nonlinear mixed-effect modeling (NONMEM) program. The proportion of midinterval concentrations 12 h after drug intake (C 12 h) corresponding to the EFV therapeutic pharmacokinetic thresholds (1 to 4 mg/liter) was assessed according to different dose regimens (25 mg/kg in the MONOD study versus the 2016 FDA recommended dose). With both the 25 mg/kg/day dose and the 2016 FDA recommended EFV dose, simulations showed that the majority of C 12 h values were within the therapeutic range (62.6% versus 62.8%). However, there were more children underexposed with the 2016 FDA recommended dose (11.6% versus 1.2%). Conversely, there were more concentrations above the threshold of toxicity with the 25 mg/kg dose (36.2% versus 25.6%), with C 12 h values of up to 15 mg/liter. Only 1 of 52 children was switched back to LPV because of persistent sleeping disorders, but his C 12 h value was within therapeutic ranges. A high EFV dose of 25 mg/kg per day in children under 3 years old achieved satisfactory therapeutic effective levels. However, the 2016 FDA recommended EFV dose appeared to provide more acceptable safe therapeutic profiles. (This study has been registered at ClinicalTrials.gov under identifier NCT01127204.)

Spectral Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect

2004 ◽  
Vol 101 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Ann L.G. Vanluchene ◽  
Hugo Vereecke ◽  
Olivier Thas ◽  
Eric P. Mortier ◽  
Steven L. Shafer ◽  
...  
Keyword(s):  
Drug Effect ◽  
Rank Correlation ◽  
Burst Suppression ◽  
Spectral Entropy ◽  
Anesthetic Drug ◽  
Mixed Effect ◽  
Nonlinear Mixed Effect ◽  
Prediction Probability ◽  
Nonlinear Mixed Effect Modeling ◽  
Mixed Effect Modeling

Background The authors compared the behavior of two calculations of electroencephalographic spectral entropy, state entropy (SE) and response entropy (RE), with the A-Line ARX Index (AAI) and the Bispectral Index (BIS) and as measures of anesthetic drug effect. They compared the measures for baseline variability, burst suppression, and prediction probability. They also developed pharmacodynamic models relating SE, RE, AAI, and BIS to the calculated propofol effect-site concentration (Ceprop). Methods With institutional review board approval, the authors studied 10 patients. All patients received 50 mg/min propofol until either burst suppression greater than 80% or mean arterial pressure less than 50 mmHg was observed. SE, RE, AAI, and BIS were continuously recorded. Ceprop was calculated from the propofol infusion profile. Baseline variability, prediction of burst suppression, prediction probability, and Spearman rank correlation were calculated for SE, RE, AAI, and BIS. The relations between Ceprop and the electroencephalographic measures of drug effect were estimated using nonlinear mixed effect modeling. Results Baseline variability was lowest when using SE and RE. Burst suppression was most accurately detected by spectral entropy. Prediction probability and individualized Spearman rank correlation were highest for BIS and lowest for SE. Nonlinear mixed effect modeling generated reasonable models relating all four measures to Ceprop. Conclusions Compared with BIS and AAI, both SE and RE seem to be useful electroencephalographic measures of anesthetic drug effect, with low baseline variability and accurate burst suppression prediction. The ability of the measures to predict Ceprop was best for BIS.

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