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PFIM 8.0

PFIM 8.0 is a full rewrite of the package on the S7 object system, with C++ (Rcpp/Armadillo) kernels for the FIM and the optimization algorithms.

Breaking changes

• New S7 object model (Evaluation, Optimization, Model, Fim, covariates, library of models). Requires R >= 4.5.0.
• Simplified public API: user verbs are run(), Report(), the get*() / plot*() accessors and the class constructors. Pipeline functions are now internal.
  • Dcriterion() -> getDcriterion()
  • plotSEFIM() / plotRSEFIM() -> plotSE() / plotRSE()
  • plotWeightsMultiplicativeAlgorithm() -> plotWeights()
  • plotFrequenciesFedorovWynnAlgorithm() -> plotFrequencies()
  • plotEvaluationResults() / plotEvaluationSI() -> plotEvaluation() / plotSensitivityIndices()
  • generateReportEvaluation() / generateReportOptimization() -> Report()
  • evaluateDesign(), optimizeDesign() and related helpers -> run()
• Individual and Bayesian FIMs are aggregated across arms and covariate strata as a covariance mixture (), not as a sum of Fisher matrices. Results are therefore not directly comparable with PFIM 6 or PopED in that case. Non-identifiable protocols with positive weight give Inf SE.

New features

• Population, individual and Bayesian FIM with categorical covariates (CategoricalCovariate) and inter-occasion variability (CategoricalCovariateWithIOV, gamma on parameters).
• Covariate effect tests: covariateTest() (alias tost()) and saveCovariateTest().
• Five optimizers with C++ kernels: Multiplicative, Fedorov-Wynn, PSO, PGBO and Simplex. Joint discrete optimization over several arms in one design.
• Discrete allocation uses Hamilton (largest-remainder) rounding so subject counts sum exactly to the study size.
• Sampling windows for continuous optimizers (samplingsWindows, numberOfTimesByWindows) with consistency checks on the initial design.
• New residual error hierarchy: Constant, Proportional, Combined1, Combined2 (additive + proportional, PopED-style).
• Observed responses can be defined as transformations of the model states through outputs in Evaluation() / Optimization() (e.g. C = A/Vd, Wout = log10(W)). Sampling times are declared on the model states (SamplingTimes(outcome = "A")) and residual error models on the observed responses (Constant(output = "C")), so the error is applied on the transformed scale (e.g. additive error on log10(W)).
• Report() generates HTML reports for evaluations and optimizations.
• Session options: pfim_get_option(), pfim_set_option(), pfim_reset_session().
• New vignettes Example01 to Example04 and LibraryOfModels. Example04 reproduces the PopED G-CSF / filgrastim PK-PD workshop (Design 1 RSE), which is also checked by the test suite.

Performance

• FIM computation, residual variance and optimization algorithms run in C++.
• Caching of FIMs, ODE solutions and finite-difference gradients; continuous optimizers reuse one evaluation context per search.

Bug fixes (relative to PFIM 7)

• Population FIM with fixed typical value and estimated IIV (fixedMu = TRUE, ): the gradient of is now computed, so the term is kept in the variance and remains estimable. fixedMu and fixedOmega are now independent.
• Rank-deficient FIMs (e.g. one sample for two parameters) now report Inf SE and Inf condition number on non-identifiable directions instead of large finite values.
• Residual-error parameters are labelled (SD scale) instead of ; values were already on the SD scale.
• ODE finite-difference steps are no longer inflated by the solver tolerance, which biased the FIM.
• Multiplicative algorithm: returned weights are certified (the optimality condition is checked before the update). Both the mixture and the realised D-criteria are returned (getMixtureDcriterion(), getRealisedDcriterion()). In joint (multi-outcome) optimization, the weights are restricted to the winning protocol, and the study size is split across its arms as in Fedorov-Wynn.
• Fedorov-Wynn: the default optimality gap is 1e-4, matching the weight precision. A run that stops on the cycle budget is reported as incomplete, with a warning, and no longer as success.
• Discrete optimizers (Multiplicative, Fedorov-Wynn): when administrationsConstraints is omitted, the dose grid is taken from the arm’s current administration.
• Simplex: increasing numberOfTimesByWindows no longer leaves the search stuck at its starting point. An infeasible start raises an error and is no longer reported as converged = TRUE.
• Continuous optimizers (Simplex, PSO, PGBO): converged is NA when tolerance <= 0 (the stopping rule is disabled). A warning is issued when the arm sampling times differ from initialSamplings, which is the actual starting point.
• Analytic models: equations of non-administered outcomes that depend on t are evaluated at their own sampling times. Disease-progression and placebo models from the library evaluate correctly again.
• A covariate effect set to 0 is kept in the population FIM, so its SE under the null hypothesis is available. A warning is issued.
• Report() restores the knitr chunk options and no longer closes the user’s graphics devices.
• Library of models: corrected closed forms for two-compartment bolus and infusion models, one-compartment infusion at steady state, exponential baseline models, sigmoid turnover and Michaelis-Menten ODE models (central volume V1). Unknown model names now raise an error.
• Constructors reject NA / non-finite parameters, unknown initial conditions and undeclared covariate categories with a clear PFIM: error.

Contributors

• Romain Leroux (aut) designed and wrote the entire source code of PFIM 8.0 (R/S7 code, C++ kernels, testthat and documentation).
• France Mentré (cre, pfim@inserm.fr) is the package maintainer.
• Vignettes:
  • Example01, Example02: Romain Leroux and Jérémy Seurat.
  • Example03: Antoine Croxo and Romain Leroux.
    
  • Example04, LibraryOfModels: Romain Leroux.
• Thanks to Antoine Croxo and Lucie Fayette for beta-testing PFIM 8.0 and running the example scripts to help verify their correct execution.

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.