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poso_*
functions. Once the model has been parsed by rxode2()
with this package the model$posologyr
gives the list needed for poso_*
functionsposo_dose_conc()
, poso_dose_auc()
and poso_inter_cmin()
where the returned estimate of the target value to be optimized against was always equal to zero.poso_time_cmin()
, poso_dose_conc()
, and poso_dose_auc()
now explicitly states the consequences of setting tdm
to TRUE
: which parameters are required, which parameters are ignored, and which parameters behave differently.poso_time_cmin()
, poso_dose_conc()
, and poso_dose_auc()
now return a warning if any of the input parameters are ignored.poso_dose_auc()
posologyr()
(as requested by CRAN)parent.frame()
(as requested by CRAN)poso_estim_map()
, poso_estim_sir()
and poso_estim_mcmc()
can now estimate individual PK profiles for multiple endpoints models (eg. PK-PD, parent-metabolite, blood-CSF…), using a different residual error model for each endpoint.poso_time_cmin()
, poso_dose_conc()
, poso_dose_auc()
and poso_inter_cmin()
now allow you to select the end point of interest for which you want to optimise, provided it is defined in the model.vignette("a_priori_dosing")
illustrates a priori dose selectionvignette("a_posteriori_dosing")
illustrates a posteriori dose selection, using TDM datavignette("auc_based_dosing")
shows how to select an optimal dose for a given target AUC using data from TDMvignette("multiple_endpoints")
introduces the new multiple endpoints featureposo_time_cmin()
can now estimate time needed to reach a selected trough concentration (Cmin) using the data from TDM directlyposo_dose_conc()
can now estimate an optimal dose to reach a target concentration following the events from TDMposo_dose_auc()
can now estimate an optimal dose to reach a target auc following the events from TDMposologyr()
is now an internal function, all exported functions take patient data and a prior model as input parametersposo_estim_map()
provides an rxode2 model using MAP-EBE and the input dataset, with interpolation of covariates, to make plotting easierposologyr()
functionposo_time_cmin()
, poso_dose_auc()
, poso_dose_conc()
, and poso_inter_cmin()
no longer fail for models with IOVposo_estim_sir()
estimates the posterior distribution of individual parameters by Sequential Importance Resampling (SIR). It is roughly 25 times faster than poso_estim_mcmc()
for 1000 samples.poso_estim_map()
allows the estimation of the individual parameters by adaptive MAP forecasting (cf. doi: 10.1007/s11095-020-02908-7) with adapt=TRUE
.poso_simu_pop()
, poso_estim_map()
, and poso_estim_sir()
now support models with both inter-individual (IIV) and inter-occasion variability (IOV).MASS:mvrnorm
is replaced by mvtnorm::rmvnorm
for multivariate normal distributions.poso_estim_map()
now uses method=“L-BFGS-B” in optim for better convergence of the algorithm.poso_inter_cmin()
now uses method=“L-BFGS-B” in optim for better convergence of the algorithm.poso_dose_conc()
is the new name of poso_dose_ctime()
.poso_time_cmin()
, poso_dose_auc()
, poso_dose_conc()
, and poso_inter_cmin()
now work with prior and posterior distributions of ETA, and not only with point estimates (such as the MAP).nocb
parameter is added to posologyr()
. The interpolation method for time-varying covariates can be either last observation carried forward (locf, the RxODE default), or next observation carried backward (nocb, the NONMEM default).vignette("uncertainty_estimates")
is removed.poso_time_cmin()
, poso_dose_ctime()
, and poso_dose_auc()
now work for multiple dose regimen.poso_inter_cmin()
allows the optimization of the inter-dose interval for multiple dose regimen.vignette("case_study_vancomycin")
illustrates AUC-based optimal dosing, multiple dose regimen, and continuous intravenous infusion.First public release.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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