![]() Further, parametric population models will not permit maximally precise drug dosing for optimal patient care, as they use only a single summary value for each model parameter, rather than the entire distribution itself, as the nonparametric approach does. If you release the distributions to be free of those constraining assumptions of normality, as with Pmetrics, then the likelihood is the better objective function. If you assume a normal distribution for the population parameters, they will be the same. Most likely results, not just the best fit. You can get Pmetrics at Download & Install The shape is determined only by the data itself. Nonparametric approaches get more likely results because they do not constrain the model parameter distributions into some assumed shape like normal or lognormal. ![]() The papers above compare the strengths and weaknesses of parametric versus nonparametric approaches to population modeling. Pmetrics is a freely available software package, embedded in R. Why are you using something in Excel? I am not clear.
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