Nonmem 7 tutorial
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These are exact methods that are able to analyze complex PK/PD problems with a greater incidence of success than FOCE. Thus, unlike FO, which linearizes the intersubject and within‐subject variability, FOCE evaluates the intersubject effect accurately while linearizing the within‐subject variability with its Gaussian function approximation. These integrations must be done for the data of each individual separately, and so FOCE takes longer to evaluate than the FO method. An approximate integral using the Gaussian function centered at the mode of the joint density with the approximate variance is used as a linear approximation of the integral of the joint density with respect to ETAs and can be easily calculated. To alleviate the computational expense, the FOCE evaluates the mode of the joint density (most likely values of ETAs) and the first order approximation of variances of ETAs. As mentioned previously, this integration is computationally expensive. In FOCE mixed effects modeling, an integral over all possible individual parameter values (ETAs, or random effects) is taken into consideration for each subject's joint density of observed data and ETAs when determining the best fixed effects (THETAs, OMEGAs, and SIGMAs). The FOCE, although also an approximate method, was more accurate for a larger variety of problems. Sometimes inaccurate assessments occurred if residual error and/or intersubject variability were large. Approximate EM methodĮxample 1: Pharmacokinetic (PK) model with covariates using IMP, SAEM, and Bayesian analysis methodsĮxample 2: Modeling data that are below the limit of quantitation (LOQ)Įxample 3: Modeling pharmacokinetic categorical response dataĮxample 4: Using ordinary differential equation (ODE) solvers to model a basic target‐mediated drug disposition modelĮxample 5: Analyzing data modeled with multiple levels of mixed effectsĮxample 6: Modeling a mixture of subpopulations of parametersĮxample 7: Modeling periodically collected urine samplesĪlthough the FO method was fast, it was very approximate. Markov Chain Monte Carlo (MCMC) stochastic approximation expectation maximization (SAEM) Monte Carlo importance sampling (IMP) expectation maximization (EM)
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Various additional methods in NONMEM are (intro7 1): The FO conditional estimation (FOCE)/Laplace method (guide VII 1)