| 1. |
- Jönsson, Siv, et al.
(author)
-
Estimating bias in population parameters for some models for repeated measures ordinal data using NONMEM or NLMIXED
- 2004
-
In: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 31:4, s. 299-320
-
Journal article (peer-reviewed)abstract
- The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral; the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. The model used for simulation was fitted to each data set for assessment of bias. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. However, markedly biased parameter estimates were obtained using the Laplacian estimation method without the centering option, in particular when distributions of observations between response categories were skewed and when the interpatient variability was moderate to large. Simulations under the model could not mimic the original data when bias was present, but resulted in overestimation of rare events. The bias was considerably reduced when the centering option in NONMEM was used. The cause for the biased estimates appears to be related to the conditioning on uninformative and uncertain empirical Bayes estimate of interindividual random effects during the estimation, in conjunction with the normality assumption.
|
|
| 2. |
- Ribbing, Jakob, et al.
(author)
-
Power, Selection Bias and Predictive Performance of the Population Pharmacokinetic Covariate Model
- 2004
-
In: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 31:2, s. 109-134
-
Journal article (peer-reviewed)abstract
- Identification and quantification of covariate relations is often an important part of population pharmacokinetic/pharmacodynamic (PK/PD) modelling. The covariate model is regularly built in a stepwise manner. With such methods, selection bias may be a problem if only statistically significant covariates are accepted into the model. Competition between multiple covariates may further increase selection bias, especially when there is a moderate to high correlation between the covariates. This can also result in a loss of power to find the true covariates. The aim of this simulation study was to investigate the effect on power, selection bias and predictive performance of the covariate model, when altering study design and system-related quantities. Data sets with 20-1000 subjects were investigated. Five covariates were created by sampling from a multivariate standard normal distribution. The true covariate was set up to have no, low, moderate and high correlation to the other four covariates, respectively. Data sets, in which each individual had two or three PK observations, were simulated using a one-compartment i.v. bolus model. The true covariate influenced clearance according to one of several magnitudes. Different magnitudes of residual error and inter-individual variability in the structural model parameters were also introduced to the simulation model. A total of 7400 replicate data sets were simulated independently for each combination of the above conditions. Models with one of the five simulated covariates influencing clearance and the model without any covariate were fitted to the data. The probability of selecting (according to a pre-specified P-value) the different covariates, along with the estimated covariate coefficient, was recorded. The results show that selection bias is very high for small data sets (< or = 50 subjects) simulated with a weak covariate effect. If selected under these circumstances, the covariate coefficient is on average estimated to be more than twice its true value, making the covariate model useless for predictive purposes. Surprisingly, even though competition from false covariates caused substantial loss in the power of selecting the true covariate, the already high selection bias increased only marginally. This means that the bias due to competition is negligible if statistical significance is also required for covariate selection. Bias and predictive performance are direct functions of power, only indirectly affected by study design and system-related quantities. Mainly because of selection bias, low-powered covariates can be expected to harm the predictive performance when selected. For the same reason these low-powered covariates may falsely appear to be clinically relevant when selected. If the aim of an analysis is predictive modelling, we do not recommend stepwise selection or significance testing of covariates to be performed on small or moderately sized data sets (<50-100 subjects).
|
|
| 3. |
- Äbelö, Angela, et al.
(author)
-
Gastric acid secretion in the dog : a mechanism-based pharmacodynamic model for histamine stimulation and irreversible inhibition by omeprazole
- 2002
-
In: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 29:4, s. 365-382
-
Journal article (peer-reviewed)abstract
- A mechanism-based pharmacodynamic model was used to describe the inhibitory effect by omeprazole on gastric acid secretion measured after histamine stimulation in the dog. The model identifies parameters that are related to the physiological system, the histamine stimulation, and the irreversible effect of omeprazole on the H+, K(+)-ATPase enzyme. Four different experiments with omeprazole (Exps. 1-4) and two placebo experiments were performed in each of the four Heidenhain pouch dogs used. For placebo and experiments 1-3, saline or omeprazole 0.81 mumol/kg was infused during 3 hr with measurements of histamine-stimulated gastric acid secretion in two periods of 3.5-6.5 hr, one period starting just before the omeprazole infusion and a second later period up to 29 hr post infusion. In experiment 4, 0.18 mumol/kg of omeprazole was infused for 22.5 min and gastric juice was collected for 5 hr post infusion. The response data was well described by the model. Similar parameter estimates were obtained by three different analysis methods; naïve pooling, two-stage method and nonlinear mixed effects modeling. The elimination rate constant for the H+, K(+)-ATPase enzyme, kout, was estimated to be 0.040 hr-1, corresponding to a half-life of about 17 hr. This rate constant determines the duration of omeprazole inhibition after long-term exposure. For short-term omeprazole exposure the duration is determined by the rate constant for transfer of enzymes from active to resting state, estimated to be 1.88 hr-1. The second-order rate constant for histamine stimulation was estimated to be 0.064 hr-1 per histamine concentration unit and the maximum acid secretion was estimated to be 5.0 mmol H+/30 min. The second-order rate constant for the irreversible binding of omeprazole to H+, K(+)-ATPase, kome, was estimated to be 2.39 L/mumol.hr. By modeling the histamine-induced baseline response simultaneously with active treatment, predictions of the response are possible not only following different dosing regimens of omeprazole, but also following different degrees of histamine stimulation.
|
|
| 4. |
|
|
