Last updated 29 July 2021
All models use both between
subject variability (BSV) and between occasion variability (BOV) for Bayesian
estimation of the individual parameters. An occasion is defined by the dosing
interval associated with any dose which has response observations (e.g.
medicine concentrations) measured before the next dose.
Two methods are available
for choosing how to predict the dose using these sources of variability. The
first method uses both BSV and BOV to predict the parameters used to calculate
the target dose. The second method uses just BSV. This is equivalent to
averaging the BOV random effects. The second method may give a more stable dose
prediction but any real changes that might have occurred from occasion to
occasion are not shown. A simulation study has shown the potential benefits of
using the averaging method (Abrantes, Jönsson et al. 2019).
It is your choice to decide
which method to use depending on what you know about the patient status and how
it might have changed from occasion to occasion. Being able to see the dose
prediction changes with dose occasion may help to identify non-random
influences such as drug interactions which are not part of the model.
When BSV and BOV are used
to estimate parameters the area under the concentration time curve (AUC) after
each dose is calculated using the occasion specific clearance. If BSV alone is
used then the AUC is calculated using the clearance averaged across all
occasions. For future predictions of clearance (for example when predicting
cumulative AUC) the BSV alone method is used to calculate clearance.
NextDose models with “_AVG”
in the name use BSV alone to predict doses. NextDose models whose name does not
include “_AVG” use both BSV and BOV to predict doses. Some models, e.g. for
warfarin, do not have BOV affecting the parameters used to predict the target
dose so the dose predictions are the same with both methods.
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Developed by Sam Holford & Nick Holford 2012-2021
Abrantes
JA, Jönsson S, Karlsson MO, Nielsen EI. Handling interoccasion variability in
model-based dose individualization using therapeutic drug monitoring data. Br J
Clin Pharmacol. 2019;85(6):1326-36