Package 'PFIM'

Description

Nonlinear mixed-effects models (NLMEM) are widely used in model-based drug development. The population Fisher information matrix (FIM) is an efficient alternative to clinical trial simulation for optimizing study designs. **PFIM 8.0.1** is an R package using the **S7** object system to evaluate and optimize population designs from the FIM.

PFIM includes libraries of PK and PD models (S7 classes): bolus, infusion, first-order absorption, one- and two-compartment structures, linear or Michaelis-Menten elimination, direct and turnover PD models, and combined PK/PD models. Users may also supply custom analytical or ODE models; set modelClass explicitly or use pfim_resolve_model_class. The FIM is computed by first-order linearization with a block-diagonal structure. Population, individual and Bayesian FIMs are available; the Bayesian FIM provides shrinkage predictions. Covariate effects can be tested with covariateTest. Design optimization under the D-criterion uses the simplex algorithm (Nelder-Mead), the multiplicative algorithm, Fedorov-Wynn, PSO (Particle Swarm Optimization) and PGBO (Population Genetics Based Optimizer).

User API

Constructors: Evaluation, Optimization, Arm, Design, ModelParameter, residual-error and distribution classes, FIM types, and algorithm classes. Verbs: run, Report, defineFim, definePKModel, definePKPDModel, getSE/getRSE/getFisherMatrix/getDcriterion/getShrinkage, plotEvaluation/plotSE/plotRSE/plotSensitivityIndices/plotWeights/plotFrequencies, showFIM, covariateTest. Session: pfim_get_option, pfim_set_option, pfim_reset_session. Pipeline helpers (evaluateDesign, optimizeDesign, ...) are internal.

Documentation

Package source, issues, and vignettes are available at http://www.pfim.biostat.fr/.

Session options

pfim_set_option / pfim_get_option; pfim_reset_session clears caches. See ?pfim_set_option for fim.cache, constraints.maxTasks, and performance options.

Covariates and inter-occasion variability

Categorical covariates and inter-occasion variability (IOV) are supported for population, individual and Bayesian FIMs; covariate effect tests use the population FIM. The number of occasions is inferred automatically from the model. Results are shown via show(), plots (ggplot2), and HTML reports.

Author(s)

Author of PFIM 8.0.1: Romain Leroux [email protected] (ORCID).

Contributors: Jérémy Seurat [email protected], Antoine Croxo [email protected], Lucie Fayette [email protected].

Maintainer: France Mentré [email protected] (ORCID).

References

Dumont C, Lestini G, Le Nagard H, Comets E, Nguyen TT, et al. PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. Comput Methods Programs Biomed. 2018;156:217-29.

Chambers JM. Object-Oriented Programming, Functional Programming and R. Stat Sci. 2014;29:167-80.

Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7:308-13.

Seurat J, Tang Y, Mentré F, Nguyen TT. Finding optimal design in nonlinear mixed effect models using multiplicative algorithms. Comput Methods Programs Biomed. 2021;207:106126.

Fedorov VV. Theory of Optimal Experiments. Academic Press, New York, 1972.

Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. Proc. of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, 4-6 October 1995, 39-43.

Le Nagard H, Chao L, Tenaillon O. The emergence of complexity and restricted pleiotropy in adapting networks. BMC Evol Biol. 2011;11:326.

Wickham H. ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, 2016.

See Also

Useful links:

• https://github.com/iame-researchCenter/PFIM

• Report bugs at https://github.com/iame-researchCenter/PFIM/issues

Description

Additive covariate link: theta(cov) = mu * (1 + beta * cov).

Usage

Additive(beta = 0, combinedEffect = 0, value = 0)

Arguments

Value

An S7 object of class Additive.

Examples

Additive(beta = 1, combinedEffect = 0.2)

Description

Dosing regimen for one outcome: dose amounts, times, infusion duration, and optional inter-dose interval (tau) for repeated dosing or steady state.

When timeDose has length 1 and dose is longer, solvers treat that as a shared administration time (see .alignAdministrationDosing).

Usage

Administration(
  outcome = character(0),
  timeDose = numeric(0),
  dose = numeric(0),
  Tinf = numeric(0),
  tau = 0
)

Arguments

Value

An S7 object of class Administration.

Examples

Administration(outcome = "RespPK", timeDose = 0, dose = 100)

Description

Allowed dose values per outcome when optimizing or reporting designs.

Used by generateDosesCombination() to build the discrete dose grid for Fedorov-Wynn / multiplicative algorithms (and constraint reports).

Usage

AdministrationConstraints(outcome = character(0), doses = list())

Arguments

Value

An S7 object of class AdministrationConstraints.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(AdministrationConstraints)

Description

One experimental arm: subjects, dosing, sampling times, and slots filled during design evaluation (model predictions, gradients, variance, FIM).

Usage

Arm(
  name = character(0),
  size = numeric(0),
  administrations = list(),
  initialConditions = list(),
  initialCondition = NULL,
  samplingTimes = list(),
  administrationsConstraints = list(),
  samplingTimesConstraints = list(),
  evaluationModel = list(),
  evaluationGradients = list(),
  evaluationVariance = list(),
  evaluationFim = NULL
)

Arguments

Value

An S7 object of class Arm.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
arm = prop(prop(ev, "designs")[[1L]], "arms")[[1L]]
length(prop(prop(arm, "samplingTimes")[[1L]], "samplings"))

Description

Bayesian Fisher information matrix with shrinkage on random effects.

First-order linearization on the random-effect scale. Covariate beta is not a subject parameter (MAP conditions on $\beta$) and is omitted. Across strata / arms, subject FIMs keep their prior and are aggregated by averaging covariances: $\bar C = \sum w_s M_s^{-1}$, $M_{\mathrm{eff}}=\bar C^{-1}$. A positive weight on a non-identifiable protocol yields Inf SE for the null-space parameters. This covariance mixture is not comparable to summing Fisher matrices (PFIM 6 / PopED on this case).

With IOV ($\gamma>0$), each occasion uses the marginal FO residual $V_k = R_k + F_k\,\mathrm{diag}(\gamma^2)\,F_k^\top$ on the $\eta$-scale (equivalent to the $\eta$ block of the augmented $(\eta,\kappa)$ system).

Usage

BayesianFim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0),
  singularFim = FALSE
)

Arguments

Value

A BayesianFim object (filled by run()).

Examples

vignette("Vignette_01_discrete_design")

Description

The class CategoricalCovariate represents a fixed categorical covariate (constant across occasions): binary or multi-level group membership (sex, genotype, centre, ...).

The constructor validates that categoriesProportions sums to 1 (within floating-point tolerance) before creating the object.

Usage

CategoricalCovariate(name, categories, categoriesProportions, effects = list())

Arguments

Value

An S7 object of class CategoricalCovariate.

Examples

CategoricalCovariate(
  name = "Sex", categories = c("M", "F"),
  categoriesProportions = c(0.5, 0.5)
)

Description

The class CategoricalCovariateWithIOV represents a categorical covariate that varies across occasions (inter-occasion variability design).

Typical use: crossover treatment sequences (AB/BA), period effects, etc.

Each element of sequences is a character vector of category labels, one per occasion. sequencesProportions gives the fraction of subjects following each sequence and must sum to 1.

Usage

CategoricalCovariateWithIOV(
  name,
  categories,
  sequences,
  sequencesProportions,
  effects = list()
)

Arguments

Value

An S7 object of class CategoricalCovariateWithIOV.

Examples

CategoricalCovariateWithIOV(
  name = "Trt",
  categories = c("A", "B"),
  sequences = list(c("A", "B"), c("B", "A")),
  sequencesProportions = c(0.5, 0.5)
)

Description

Combined residual error (additive + proportional SD): variance (sigmaInter + sigmaSlope * f^{cError})^2.

Usage

Combined1(
  output = character(0),
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1,
  ...
)

Arguments

Value

An S7 object of class Combined1.

Examples

Combined1(output = "RespPK", sigmaInter = 0.5, sigmaSlope = 0.1)

Description

Combined residual error with independent additive and proportional SDs: variance sigmaInter^2 + (sigmaSlope * f^{cError})^2. Matches PopED y*(1+e_prop)+e_add when both sigmas are residual SDs.

Usage

Combined2(
  output = character(0),
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1,
  ...
)

Arguments

Value

An S7 object of class Combined2.

Examples

Combined2(output = "RespPK", sigmaInter = 0.5, sigmaSlope = 0.1)

Description

Pure additive residual error: V = sigmaInter^2 (combined1 with sigmaSlope locked to 0).

Usage

Constant(
  output = character(0),
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1,
  ...
)

Arguments

Value

An S7 object of class Constant.

Examples

Constant(output = "RespPK", sigmaInter = 0.1)

Description

The class Covariate is the base class for all covariate types.

The constructor doubles as a smart factory: when categories is supplied it dispatches to the correct concrete subclass:

• sequences + sequencesProportions -> CategoricalCovariateWithIOV

• categoriesProportions -> CategoricalCovariate

• otherwise -> plain Covariate

Usage

Covariate(
  name,
  effects = list(),
  categories = NULL,
  categoriesProportions = NULL,
  sequences = NULL,
  sequencesProportions = NULL
)

Arguments

Value

An S7 object of class Covariate.

Examples

Covariate(name = "Sex", categories = c("M", "F"),
categoriesProportions = c(0.5, 0.5), effects = list(F = c(V = log(1.2))))

Description

The class CovariateModelEquation is the base class for the two covariate structural equations supported by PFIM: Additive and Exponential.

A covariate model equation adjusts the population mean for individual i with covariate value

• Additive: $\theta = \mu \cdot (1 + \beta \cdot \mathrm{cov})$

• Exponential: $\theta = \mu \cdot \exp(\beta \cdot \mathrm{cov})$

Concrete subclasses implement computeCovariateValue().

Usage

CovariateModelEquation(beta = 0, combinedEffect = 0, value = 0)

Arguments

Value

An S7 object of class CovariateModelEquation.

Examples

CovariateModelEquation(beta = 1, combinedEffect = 0.2)

Description

CovariateTest stores covariate-test results.

Usage

CovariateTest(
  covariate = data.frame(),
  parameter = data.frame(),
  nonRelevance = data.frame(),
  relevance = data.frame(),
  settings = list()
)

Arguments

Details

covariate

Significance on covariate effects $\beta$

nonRelevance

TOST non-relevance on $\beta$

relevance

Clinical relevance on $\beta$

settings

Run options (tests, thetaL/U, targetPower, alpha, N0)

Value

An S7 object of class CovariateTest.

Examples

source(system.file("examples", "covariate-test-minimal.R", package = "PFIM"))
class(ct)

Description

Experimental design: one or more arms, each with dosing and sampling schedules. Filled by evaluateDesign() with per-arm FIM results.

Usage

Design(
  name = character(0),
  size = 0,
  arms = list(),
  evaluationArms = list(),
  numberOfArms = 0,
  fim = NULL
)

Arguments

Value

An S7 object of class Design.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
design = prop(ev, "designs")[[1L]]
prop(prop(design, "arms")[[1L]], "size")

Description

Base class for parameter distributions (Normal, LogNormal).

Usage

Distribution(name = character(0), mu = 0, omega = 0)

Arguments

Value

An S7 object of class Distribution.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(Distribution)

Description

Design evaluation: build the model, compute the Fisher information matrix (FIM), and store results per design. Inherits PFIMProject fields directly. Use prop for project fields on both Evaluation and Optimization. After run(), getEvaluationDesign returns per-design results when several designs were evaluated.

Equation strings may use declared ModelParameter names, t, dose_* / Tinf_* for administered outcomes, and other outcome names. Free symbols resolve in knitr::knit_global() (while knitting) or globalenv(). Names that already exist on the search path (beta, gamma, pi) are not reported as missing; declare every model parameter explicitly.

Usage

Evaluation(
  evaluationDesign = list(),
  name = character(0),
  modelClass = character(0),
  modelParameters = list(),
  modelCovariates = list(),
  modelCovariatesEquation = character(0),
  modelEquations = list(),
  modelFromLibrary = list(),
  modelError = list(),
  designs = list(),
  outputs = list(),
  fimType = character(0),
  odeSolverParameters = list(),
  numberOfOccasions = NA_real_,
  fim = NULL
)

Arguments

Value

An Evaluation object with evaluationDesign and fim filled.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))

Description

Exponential covariate link: theta(cov) = mu * exp(beta * cov).

Usage

Exponential(beta = 0, combinedEffect = 0, value = 0)

Arguments

Value

An S7 object of class Exponential.

Examples

Exponential(beta = 1, combinedEffect = 0.2)

Description

Fedorov-Wynn exchange algorithm for D-optimal sampling-time design (Rcpp kernel). Pass elementaryProtocols, numberOfSubjects, and proportionsOfSubjects via optimizerParameters on Optimization. Optional delta (default 1e-4) is the relative optimality gap $\max_i \phi_i \le p(1+\delta)$, same role as in the multiplicative algorithm. Each elementary protocol may be a flat numeric vector (full grid row) or a list of per-outcome vectors that concatenate to that row (typical PK/PD). A vignette-style list(pk, pd) with a single proportion is accepted as one multi-outcome protocol.

Usage

FedorovWynnAlgorithm(FedorovWynnAlgorithmOutputs = list())

Arguments

Details

FedorovWynnAlgorithm: S7 class

Value

A FedorovWynnAlgorithm specification object for run(Optimization).

Examples

vignette("Vignette_01_discrete_design")

Description

Base class for Fisher information matrices (PopulationFim, IndividualFim, BayesianFim).

Usage

Fim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0),
  singularFim = FALSE
)

Arguments

Value

An S7 object holding FIM matrices, SE/RSE tables, and condition numbers.

Examples

PopulationFim()

Description

Retrieve correlation matrix from results

Parameter correlations from the optimal design FIM.

Parameter correlations from the evaluated design FIM.

Usage

getCorrelationMatrix(pfimproject, ...)

Arguments

Value

Correlation matrix of the parameter estimates.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(getCorrelationMatrix)

Description

Build HTML tables from a covariate test result

Usage

getCovariateTestTables(covariateTestResult, tests = NULL)

Arguments

Value

Named list of knitr_kable objects (or NULL if empty). Element significance is the covariate slot (report alias).

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(getCovariateTestTables)

Description

Retrieve D-criterion from project results

D-criterion of the optimal design.

D-criterion of the evaluated design.

Usage

getDcriterion(pfimproject, ...)

Arguments

Value

Numeric D-optimality criterion value.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
getDcriterion(ev)

Description

Retrieve determinant of the Fisher matrix

Determinant of the optimal design FIM.

Determinant of the evaluated design FIM.

Usage

getDeterminant(pfimproject, ...)

Arguments

Value

Numeric determinant of the Fisher information matrix.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
getDeterminant(ev)

Description

Per-design evaluation result from a multi-design run().

Usage

getEvaluationDesign(evaluation, design = 1L)

Arguments

Value

One element of evaluationDesign (evaluated design with fim).

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(getEvaluationDesign)

Description

Runs setEvaluationFim() so dimnames and SE/RSE match the current model metadata, then returns the matrix blocks used by report helpers, plus singularFim.

Usage

getFisherMatrix(pfimproject, ...)

Arguments

Value

A list with fisherMatrix, fixedEffects, varianceEffects, and singularFim.

List with fisherMatrix, fixedEffects, varianceEffects, and singularFim.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
getFisherMatrix(ev)

Description

Differs from getDcriterion() (implemented protocol) when joint cells or Hamilton allocation change the realised design. Requires MultiplicativeAlgorithm results.

Usage

getMixtureDcriterion(pfimproject, ...)

Arguments

Value

Numeric D-criterion of the continuous mixture.

Examples

algo = MultiplicativeAlgorithm(
  multiplicativeAlgorithmOutputs = list(mixtureDcriterion = 2, realisedDcriterion = 1.5)
)
opt = Optimization(
  name = "mult_d",
  modelFromLibrary = list(PKModel = "Linear1BolusSingleDose_kV"),
  modelParameters = list(
    ModelParameter(name = "k", distribution = LogNormal(mu = 0.25, omega = 0.5)),
    ModelParameter(name = "V", distribution = LogNormal(mu = 15, omega = 0.3))
  ),
  modelError = list(Combined1(output = "RespPK", sigmaInter = 0.5, sigmaSlope = 0.15)),
  optimizer = "MultiplicativeAlgorithm",
  optimizerParameters = list(
    lambda = 0.99, delta = 1e-3, numberOfIterations = 20L, weightThreshold = 0.01
  ),
  outputs = list("RespPK"),
  designs = list(Design(
    name = "d",
    arms = list(Arm(
      name = "a", size = 10,
      administrations = list(Administration(outcome = "RespPK", timeDose = 0, dose = 100)),
      samplingTimes = list(SamplingTimes(outcome = "RespPK", samplings = c(1, 4)))
    ))
  )),
  fimType = "individual",
  optimisationAlgorithmOutputs = list(
    optimizationAlgorithm = algo,
    mixtureDcriterion = 2,
    realisedDcriterion = 1.5
  )
)
getMixtureDcriterion(opt)

Description

Parameter table for reports (mu, omega2, gamma2, fix flags, distribution).

Usage

getModelParametersData(modelParameter, ...)

Arguments

Value

A data frame of model parameter values.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(getModelParametersData)

Description

Matches getDcriterion() on the optimal evaluation. Requires MultiplicativeAlgorithm results.

Usage

getRealisedDcriterion(pfimproject, ...)

Arguments

Value

Numeric D-criterion of the implemented protocol.

Examples

algo = MultiplicativeAlgorithm(
  multiplicativeAlgorithmOutputs = list(mixtureDcriterion = 2, realisedDcriterion = 1.5)
)
opt = Optimization(
  name = "mult_d",
  modelFromLibrary = list(PKModel = "Linear1BolusSingleDose_kV"),
  modelParameters = list(
    ModelParameter(name = "k", distribution = LogNormal(mu = 0.25, omega = 0.5)),
    ModelParameter(name = "V", distribution = LogNormal(mu = 15, omega = 0.3))
  ),
  modelError = list(Combined1(output = "RespPK", sigmaInter = 0.5, sigmaSlope = 0.15)),
  optimizer = "MultiplicativeAlgorithm",
  optimizerParameters = list(
    lambda = 0.99, delta = 1e-3, numberOfIterations = 20L, weightThreshold = 0.01
  ),
  outputs = list("RespPK"),
  designs = list(Design(
    name = "d",
    arms = list(Arm(
      name = "a", size = 10,
      administrations = list(Administration(outcome = "RespPK", timeDose = 0, dose = 100)),
      samplingTimes = list(SamplingTimes(outcome = "RespPK", samplings = c(1, 4)))
    ))
  )),
  fimType = "individual",
  optimisationAlgorithmOutputs = list(
    optimizationAlgorithm = algo,
    mixtureDcriterion = 2,
    realisedDcriterion = 1.5
  )
)
getRealisedDcriterion(opt)

Description

Retrieve relative standard errors from results

Relative standard errors from the optimal design FIM (after run()).

Relative standard errors (%) from the evaluated design FIM.

Usage

getRSE(pfimproject, ...)

Arguments

Value

A list or matrix of relative standard errors (percent).

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
getRSE(ev)

Description

Retrieve standard errors from results

Standard errors from the optimal design FIM (after run()).

Standard errors from the evaluated design FIM.

Usage

getSE(pfimproject, ...)

Arguments

Value

A list or matrix of standard errors for the FIM parameters.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
getSE(ev)

Description

Retrieve shrinkage metrics from results

Parameter shrinkage from the Bayesian optimal-design FIM.

Parameter shrinkage from a Bayesian evaluation FIM (empty for other types).

Usage

getShrinkage(pfimproject, ...)

Arguments

Value

Numeric vector or list of Bayesian shrinkage values.

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))
# help(getShrinkage)

Description

Individual (subject-level) Fisher information matrix.

Across covariate strata (and arms at design level), subject FIMs are combined by averaging covariances: $\bar C = \sum_s w_s M_s^{-1}$, $M_{\mathrm{eff}} = \bar C^{-1}$. A positive weight on a non-identifiable protocol yields Inf SE. This mixture is not comparable to summing Fisher matrices (PFIM 6 / PopED). Covariate beta effects are not subject parameters and are omitted. With IOV, each occasion uses $V_k = R_k + F_k\,\mathrm{diag}(\gamma^2)\,F_k^\top$ for the $\mu$ and residual $\sigma$ blocks.

Arm-level matrices are per-subject.

Usage

IndividualFim(
  fisherMatrix = numeric(0),
  fixedEffects = numeric(0),
  varianceEffects = numeric(0),
  SEAndRSE = list(),
  condNumberFixedEffects = 0,
  condNumberVarianceEffects = 0,
  shrinkage = numeric(0),
  singularFim = FALSE
)

Arguments

Details

Block-diagonal structure for a single stratum:

$M_I = \mathrm{bdiag}(M_\mu, M_\sigma)$

where $M_\mu = G^\top V^{-1} G$ and $M_\sigma$ is the residual variance-effects block (with the same $V$ under IOV).

Value

An IndividualFim object (filled by run()).

Examples

source(system.file("examples", "evaluation-minimal.R", package = "PFIM"))

Description

Registry of built-in PK and PD model definitions.

Subclasses LibraryOfPKModels and LibraryOfPDModels store named equation lists; defineModelEquationsFromLibraryOfModel() looks them up via the package instances pkModelLibrary / pdModelLibrary.

Usage

LibraryOfModels(models = list())

Arguments

Value

An S7 object of class LibraryOfModels.

Examples

length(prop(pkModelLibrary, "models"))
length(prop(pdModelLibrary, "models"))

Description

Built-in pharmacodynamic (PD) model library (Emax, turnover, etc.).

Entries are named lists of equation strings (RespPD for immediate response, Deriv_E for turnover ODEs). Combined with a PK model via definePKPDModel().

Usage

LibraryOfPDModels(models = list())

Arguments

Value

An S7 object of class LibraryOfPDModels.

Examples

names(prop(pdModelLibrary, "models"))[1:5]

Description

Built-in pharmacokinetic (PK) model library (bolus, infusion, absorption, one- and two-compartment and Michaelis-Menten structures).

Exported Linear* helpers return equation *strings* (not S7 objects): two-compartment forms expand hybrid rates (alpha/beta) and partial-fraction coefficients (A/B) via string substitution. Prefer looking up names in pkModelLibrary for new code.

Usage

LibraryOfPKModels(models = list())

Arguments

Value

An S7 object of class LibraryOfPKModels.

Examples

names(prop(pkModelLibrary, "models"))[1:5]
nzchar(Linear2BolusSingleDose_ClQV1V2())

Description

Two-compartment IV bolus (Cl, Q, V1, V2 parameterization).

Usage

Linear2BolusSingleDose_ClQV1V2()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2BolusSingleDose_ClQV1V2())

Description

Analytic PK equation: Linear2BolusSingleDose_kk12k21V (micro-constant form).

Usage

Linear2BolusSingleDose_kk12k21V()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2BolusSingleDose_kk12k21V())

Description

Analytic PK equation: Linear2BolusSteadyState_ClQV1V2tau.

Usage

Linear2BolusSteadyState_ClQV1V2tau()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2BolusSteadyState_ClQV1V2tau())

Description

Analytic PK equation: Linear2BolusSteadyState_kk12k21Vtau.

Usage

Linear2BolusSteadyState_kk12k21Vtau()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2BolusSteadyState_kk12k21Vtau())

Description

Analytic PK equation: Linear2FirstOrderSingleDose_kaClQV1V2.

Usage

Linear2FirstOrderSingleDose_kaClQV1V2()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2FirstOrderSingleDose_kaClQV1V2())

Description

Analytic PK equation: Linear2FirstOrderSingleDose_kakk12k21V.

Usage

Linear2FirstOrderSingleDose_kakk12k21V()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2FirstOrderSingleDose_kakk12k21V())

Description

Analytic PK equation: Linear2FirstOrderSteadyState_kaClQV1V2tau.

Usage

Linear2FirstOrderSteadyState_kaClQV1V2tau()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2FirstOrderSteadyState_kaClQV1V2tau())

Description

Analytic PK equation: Linear2FirstOrderSteadyState_kakk12k21Vtau.

Usage

Linear2FirstOrderSteadyState_kakk12k21Vtau()

Value

Character string: closed-form PK equation.

Examples

nzchar(Linear2FirstOrderSteadyState_kakk12k21Vtau())

Description

Analytic PK equation: Linear2InfusionSingleDose_ClQV1V2.

Usage

Linear2InfusionSingleDose_ClQV1V2()

Value

Named list with duringInfusion / afterInfusion equation strings.

Examples

nzchar(Linear2InfusionSingleDose_ClQV1V2()$duringInfusion$RespPK)

Description

Analytic PK equation: Linear2InfusionSingleDose_kk12k21V.

Usage

Linear2InfusionSingleDose_kk12k21V()

Value

Named list with duringInfusion / afterInfusion equation strings.

Examples

nzchar(Linear2InfusionSingleDose_kk12k21V()$duringInfusion$RespPK)

Description

Analytic PK equation: Linear2InfusionSteadyState_ClQV1V2tau.

Usage

Linear2InfusionSteadyState_ClQV1V2tau()

Value

Named list with duringInfusion / afterInfusion equation strings.

Examples

nzchar(Linear2InfusionSteadyState_ClQV1V2tau()$duringInfusion$RespPK)

Description

Analytic PK equation: Linear2InfusionSteadyState_kk12k21Vtau.

Usage

Linear2InfusionSteadyState_kk12k21Vtau()

Value

Named list with duringInfusion / afterInfusion equation strings.

Examples

nzchar(Linear2InfusionSteadyState_kk12k21Vtau()$duringInfusion$RespPK)

Description

Log-normal distribution for positive PK/PD parameters (default in PFIM).

Usage

LogNormal(name = character(0), mu = 0, omega = 0)

Arguments

Value

An S7 object of class LogNormal.

Examples

LogNormal(mu = 1, omega = 0.3)

Description

Internal model object built from a PFIMProject: parameters, covariates, equations, and evaluation hooks (gradients, variance).

Usage

Model(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

Value

An S7 object of class Model.

Examples

Model()

Description

Closed-form (non-ODE) PK/PD model with bolus or explicit dosing.

Usage

ModelAnalytic(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalytic = list(),
  functionArgumentsModelAnalytic = list(),
  functionArgumentsSymbolModelAnalytic = list(),
  solverInputs = list()
)

Arguments

Value

An S7 object of class ModelAnalytic.

Examples

ModelAnalytic()

Description

Closed-form model with infusion (dose in equations). Equations come as duringInfusion / afterInfusion pairs; evaluation superposes past doses (after formula) with the active infusion (during formula) using half-open windows $[t_{\mathrm{dose}},\, t_{\mathrm{dose}}+T_{\mathrm{inf}})$.

Usage

ModelAnalyticInfusion(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalyticInfusion = list(),
  functionArgumentsModelAnalyticInfusion = list(),
  functionArgumentsSymbolModelAnalyticInfusion = list(),
  solverInputs = list()
)

Arguments

Value

The requested PFIM result.

Examples

ModelAnalyticInfusion()

Description

Analytic infusion model at steady state.

Usage

ModelAnalyticInfusionSteadyState(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalyticInfusion = list(),
  functionArgumentsModelAnalyticInfusion = list(),
  functionArgumentsSymbolModelAnalyticInfusion = list(),
  solverInputs = list()
)

Arguments

Value

An S7 object of class ModelAnalyticInfusionSteadyState.

Examples

ModelAnalyticInfusionSteadyState()

Description

Analytic model at steady state (tau in equations).

Usage

ModelAnalyticSteadyState(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  wrapperModelAnalytic = list(),
  functionArgumentsModelAnalytic = list(),
  functionArgumentsSymbolModelAnalytic = list(),
  solverInputs = list()
)

Arguments

Details

ModelAnalyticSteadyState

Value

An S7 object of class ModelAnalyticSteadyState.

Examples

ModelAnalyticSteadyState()

Description

Base class for residual error models (Constant, Combined1, Combined2, Proportional).

Variance is determined by sigmaInter (additive SD), sigmaSlope (proportional SD), cError, and varianceForm:

• "combined1": $V=(a + b f^{c})^2$

• "combined2": $V=a^2 + (b f^{c})^2$ (PopED-style)

A sigma is estimable in the FIM iff it is non-zero and not marked fixed (sigmaInterFixed / sigmaSlopeFixed).

Usage

ModelError(
  output = "output",
  sigmaInter = 0,
  sigmaSlope = 0,
  sigmaInterFixed = FALSE,
  sigmaSlopeFixed = FALSE,
  cError = 1,
  varianceForm = "combined1",
  ...
)

Arguments

Value

An S7 object of class ModelError.

Examples

ModelError()

Description

Base class for infusion administration models.

Usage

ModelInfusion(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

Value

An S7 object of class ModelInfusion.

Examples

ModelInfusion()

Description

Base class for models integrated with deSolve.

Usage

ModelODE(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

Value

An S7 object of class ModelODE.

Examples

ModelODE()

Description

ODE bolus model with doses in initial conditions.

Usage

ModelODEBolus(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODE = function() NULL,
  doseEvent = list(),
  solverInputs = list()
)

Arguments

Value

An S7 object of class ModelODEBolus.

Examples

ModelODEBolus()

Description

ODE model with bolus dose terms inside the differential equations.

Usage

ModelODEDoseInEquations(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODEDoseInEquations = function() NULL,
  solverInputs = list()
)

Arguments

Value

An S7 object of class ModelODEDoseInEquations.

Examples

ModelODEDoseInEquations()

Description

ODE model with bolus doses added as compartment events.

Usage

ModelODEDoseNotInEquations(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODE = function() NULL,
  doseEvent = list(),
  solverInputs = list()
)

Arguments

Value

An S7 object of class ModelODEDoseNotInEquations.

Examples

ModelODEDoseNotInEquations()

Description

ODE model with infusion inputs.

Inherits from ModelODE (numerical nature) rather than ModelInfusion, so S7_inherits(x, ModelODE) is TRUE for infusion ODEs. Use .pfimIsInfusionModel() for the infusion capability.

Usage

ModelODEInfusion(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list()
)

Arguments

Value

An S7 object of class ModelODEInfusion.

Examples

ModelODEInfusion()

Description

ODE infusion model with dose terms in the equations.

Usage

ModelODEInfusionDoseInEquation(
  name = character(0),
  modelParameters = list(),
  modelCovariatesEquation = NULL,
  modelCovariates = list(),
  covariatesEffect = list(),
  covariatesCombination = list(),
  modelParametersWithCovariates = list(),
  numberOfOccasions = 1,
  omegaWithIOV = numeric(0),
  samplings = numeric(0),
  modelEquations = list(),
  wrapper = NULL,
  outputFormula = list(),
  outputNames = character(0),
  variableNames = character(0),
  outcomesWithAdministration = character(0),
  outcomesWithNoAdministration = character(0),
  modelError = list(),
  odeSolverParameters = list(),
  parametersForComputingGradient = list(),
  initialConditions = numeric(0),
  functionArguments = character(0),
  functionArgumentsSymbol = list(),
  modelODE = function() NULL,
  wrapperModelInfusion = list(),
  solverInputs = list()
)

Arguments

Value

An S7 object of class ModelODEInfusionDoseInEquation.

Examples

ModelODEInfusionDoseInEquation()

Description

One population model parameter: distribution (mu, omega), optional IOV (gamma), and fix flags.

Usage

ModelParameter(
  name = character(0),
  gamma = 0,
  distribution = NULL,
  fixedMu = FALSE,
  fixedOmega = FALSE,
  value = numeric(0)
)

Arguments

Value

An S7 object of class ModelParameter.

Examples

ModelParameter(name = "Cl", distribution = LogNormal(mu = 1, omega = 0.3))

Description

Multiplicative weight algorithm for optimal sampling-time allocation (Rcpp kernel). Pass lambda, delta, numberOfIterations, and weightThreshold via optimizerParameters on Optimization.

Outputs store two D-values: mixtureDcriterion is the D of the continuous mixture over retained candidate cells; realisedDcriterion is the D of the single protocol actually implemented. For joint multi-outcome cells (several arms sharing one protocol in the constraint grid), only the best-weighted cell is kept - mixture weights describe the optimisation simplex, not a multi-protocol subject allocation across joint arms.

Usage

MultiplicativeAlgorithm(multiplicativeAlgorithmOutputs = list())

Arguments

Value

A MultiplicativeAlgorithm specification object.

Examples

vignette("Vignette_01_discrete_design")

Description

Normal distribution for model parameters (identity link on the mean).

Usage

Normal(name = character(0), mu = 0, omega = 0)

Arguments