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poso_simu_pop() in v1.2.5 introduced
several issues and have been revertedposo_replace_et() enables updating a
model with events from a new rxode2 event table, while accounting for
and interpolating any covariates or inter-occasion variabilityposo_time_cmin(), poso_dose_conc(),
poso_dose_auc() and poso_inter_cmin().poso_simu_pop() provides an rxode2 model using the
simulated ETA and the input dataset, with interpolation of covariates,
to make plotting easiervignette("route_of_administration") shows how to select
a route of administration for optimal dosingvignette("population_models") describes the structure
of prior population models written as model functions which can be
parsed by rxode2 and used by posologyrvignette("posologyr_user_defined_models") is renamed
vignette("classic_posologyr_models")rxode2 model functionsposo_estim_map(),
poso_estim_sir() and poso_simu_pop() failed
for models featuring a single parameter with IIV.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.