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.