poso_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 posologyr
vignette("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.