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posologyr v1.2.4

  • Add ability to use rxode2 ui models for the poso_* functions. Once the model has been parsed by rxode2() with this package the model$posologyr gives the list needed for poso_* functions

posologyr v1.2.3

Bug fix

  • Fix a bug in poso_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.

Documentation

  • The documentation for 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.
  • The functions poso_time_cmin(), poso_dose_conc(), and poso_dose_auc() now return a warning if any of the input parameters are ignored.
  • Fix incorrect information regarding the duration of the AUC in the documentation of poso_dose_auc()

posologyr v1.2.2

  • Relax the requirements of the NONMEM comparison test for time-varying covariates to account for computational differences observed with the alternative BLAS ATLAS on CRAN.

posologyr v1.2.1

  • Add a reference to Kang et al. (2012) doi:10.4196/kjpp.2012.16.2.97 in the DESCRIPTION (as requested by CRAN)
  • Fix messages to the console in the internal function posologyr() (as requested by CRAN)
  • Fix assignment to parent environment in dose optim functions, using parent.frame() (as requested by CRAN)

posologyr v1.2.0

Additional features

  • 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.

Documentation

  • vignette("a_priori_dosing") illustrates a priori dose selection
  • vignette("a_posteriori_dosing") illustrates a posteriori dose selection, using TDM data
  • vignette("auc_based_dosing") shows how to select an optimal dose for a given target AUC using data from TDM
  • vignette("multiple_endpoints") introduces the new multiple endpoints feature

Internal changes

  • The description of the package is updated

posologyr v1.1.0

Additional features

  • poso_time_cmin() can now estimate time needed to reach a selected trough concentration (Cmin) using the data from TDM directly
  • poso_dose_conc() can now estimate an optimal dose to reach a target concentration following the events from TDM
  • poso_dose_auc() can now estimate an optimal dose to reach a target auc following the events from TDM

posologyr v1.0.0

Breaking changes

  • posologyr() is now an internal function, all exported functions take patient data and a prior model as input parameters
  • The adaptive MAP forecasting option is removed

Additional features

  • poso_estim_map() provides an rxode2 model using MAP-EBE and the input dataset, with interpolation of covariates, to make plotting easier

Internal changes

  • RxODE import is updated to rxode2
  • All tests are updated to take into account the internalization of the posologyr() function

Bug fixes

  • poso_time_cmin(), poso_dose_auc(), poso_dose_conc(), and poso_inter_cmin() no longer fail for models with IOV

posologyr v0.2.0

  • poso_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.
  • Input validation is added to all exported functions.
  • 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().
  • Issues #5 and #6 are fixed: 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).
  • A new 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.
  • The built-in models are removed.

posologyr v0.1.1

  • 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.

posologyr v0.1.0

First public release.