Comput Methods Programs Biomed - NIMROD: a program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations.

Tópicos

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Resumo

Models based on ordinary differential equations (ODE) are widespread tools for describing dynamical systems. In biomedical sciences, data from each subject can be sparse making difficult to precisely estimate individual parameters by standard non-linear regression but information can often be gained from between-subjects variability. This makes natural the use of mixed-effects models to estimate population parameters. Although the maximum likelihood approach is a valuable option, identifiability issues favour Bayesian approaches which can incorporate prior knowledge in a flexible way. However, the combination of difficulties coming from the ODE system and from the presence of random effects raises a major numerical challenge. Computations can be simplified by making a normal approximation of the posterior to find the maximum of the posterior distribution (MAP). Here we present the NIMROD program (normal approximation inference in models with random effects based on ordinary differential equations) devoted to the MAP estimation in ODE models. We describe the specific implemented features such as convergence criteria and an approximation of the leave-one-out cross-validation to assess the model quality of fit. In pharmacokinetics models, first, we evaluate the properties of this algorithm and compare it with FOCE and MCMC algorithms in simulations. Then, we illustrate NIMROD use on Amprenavir pharmacokinetics data from the PUZZLE clinical trial in HIV infected patients.

Resumo Limpo

model base ordinari differenti equat ode widespread tool describ dynam system biomed scienc data subject can spars make difficult precis estim individu paramet standard nonlinear regress inform can often gain betweensubject variabl make natur use mixedeffect model estim popul paramet although maximum likelihood approach valuabl option identifi issu favour bayesian approach can incorpor prior knowledg flexibl way howev combin difficulti come ode system presenc random effect rais major numer challeng comput can simplifi make normal approxim posterior find maximum posterior distribut map present nimrod program normal approxim infer model random effect base ordinari differenti equat devot map estim ode model describ specif implement featur converg criteria approxim leaveoneout crossvalid assess model qualiti fit pharmacokinet model first evalu properti algorithm compar foce mcmc algorithm simul illustr nimrod use amprenavir pharmacokinet data puzzl clinic trial hiv infect patient

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