Package 'posologyr' reference manual

Title:	Individual Dose Optimization using Population Pharmacokinetics
Description:	Optimize drug regimens through model-informed precision dosing, using individual pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) profiles. By integrating therapeutic drug monitoring (TDM) data with population models, 'posologyr' provides accurate posterior estimates and enables the calculation of personalized dosing regimens. The empirical Bayes estimates are computed following the method described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.
Authors:	Cyril Leven [aut, cre, cph] , Matthew Fidler [ctb] , Emmanuelle Comets [ctb], Audrey Lavenu [ctb], Marc Lavielle [ctb]
Maintainer:	Cyril Leven <[email protected]>
License:	AGPL-3
Version:	1.2.7
Built:	2024-11-08 17:31:31 UTC
Source:	https://github.com/levenc/posologyr

Estimate the dose needed to reach a target area under the concentration-time curve (AUC)

Description

estimates the dose needed to reach a target area under the concentration-time curve (AUC) given a population pharmacokinetic model, a set of individual parameters, and a target AUC.

Usage

poso_dose_auc(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  time_auc,
  time_dose = NULL,
  cmt_dose = 1,
  target_auc,
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_time = 0,
  interdose_interval = NULL,
  add_dose = NULL,
  duration = 0,
  starting_dose = 100,
  indiv_param = NULL
)
poso_dose_auc(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  time_auc,
  time_dose = NULL,
  cmt_dose = 1,
  target_auc,
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_time = 0,
  interdose_interval = NULL,
  add_dose = NULL,
  duration = 0,
  starting_dose = 100,
  indiv_param = NULL
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`tdm`	A boolean. If `TRUE`: estimates the optimal dose for a selected target auc over a selected duration following the events from `dat`, and using Maximum A Posteriori estimation. Setting `tdm` to `TRUE` causes the following to occur: the `time_dose` argument is required and is used as the starting point for the AUC calculation instead of `starting_time`; the arguments `estim_method`, `p`, `greater_than`, `interdose_interval`, `add_dose`, `indiv_param` and `starting_time` are ignored.
`time_auc`	Numeric. A duration. The target AUC is computed from `starting_time` to `starting_time` + `time_auc`. When `tdm` is set to `TRUE` the target AUC is computed from `time_dose` to `time_dose` + `time_auc` instead.
`time_dose`	Numeric. Time when the dose is to be given. Only used and mandatory, when `tdm` is set to `TRUE`.
`cmt_dose`	Character or numeric. The compartment in which the dose is to be administered. Must match one of the compartments in the prior model. Defaults to 1.
`target_auc`	Numeric. The target AUC.
`estim_method`	A character string. An estimation method to be used for the individual parameters. The default method "map" is the Maximum A Posteriori estimation, the method "prior" simulates from the prior population model, and "sir" uses the Sequential Importance Resampling algorithm to estimate the a posteriori distribution of the individual parameters. This argument is ignored if `indiv_param` is provided, or if `tdm` is set to `TRUE`.
`nocb`	A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.
`p`	Numeric. The proportion of the distribution of AUC to consider for the optimization. Mandatory for `estim_method=sir`. This argument is ignored if `tdm` is set to `TRUE`.
`greater_than`	A boolean. If `TRUE`: targets a dose leading to a proportion `p` of the AUCs to be greater than `target_auc`. Respectively, lower if `FALSE`. This argument is ignored if `tdm` is set to `TRUE`.
`starting_time`	Numeric. First point in time of the AUC, for multiple dose regimen. The default is zero. This argument is ignored if `tdm` is set to `TRUE`, and `time_dose` is used as a starting point instead.
`interdose_interval`	Numeric. Time for the interdose interval for multiple dose regimen. Must be provided when add_dose is used. This argument is ignored if `tdm` is set to `TRUE`.
`add_dose`	Numeric. Additional doses administered at inter-dose interval after the first dose. Optional. This argument is ignored if `tdm` is set to `TRUE`.
`duration`	Numeric. Duration of infusion, for zero-order administrations.
`starting_dose`	Numeric. Starting dose for the optimization algorithm.
`indiv_param`	Optional. A set of individual parameters : THETA, estimates of ETA, and covariates. This argument is ignored if `tdm` is set to `TRUE`.

Value

A list containing the following components:

dose: Numeric. An optimal dose for the selected target AUC.
type_of_estimate: Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
auc_estimate: A vector of numeric estimates of the AUC. Either a single value (for a point estimate of ETA), or a distribution.
indiv_param: A data.frame. The set of individual parameters used for the determination of the optimal dose : THETA, estimates of ETA, and covariates

Examples

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the optimal dose to reach an AUC(0-12h) of 45 h.mg/l
poso_dose_auc(dat=df_patient01,prior_model=mod_run001,
time_auc=12,target_auc=45)

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the optimal dose to reach an AUC(0-12h) of 45 h.mg/l
poso_dose_auc(dat=df_patient01,prior_model=mod_run001,
time_auc=12,target_auc=45)

Estimate the optimal dose to achieve a target concentration at any given time

Description

Estimates the optimal dose to achieve a target concentration at any given time given a population pharmacokinetic model, a set of individual parameters, a selected point in time, and a target concentration.

Usage

poso_dose_conc(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  time_c,
  time_dose = NULL,
  target_conc,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_dose = 100,
  interdose_interval = NULL,
  add_dose = NULL,
  duration = 0,
  indiv_param = NULL
)
poso_dose_conc(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  time_c,
  time_dose = NULL,
  target_conc,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_dose = 100,
  interdose_interval = NULL,
  add_dose = NULL,
  duration = 0,
  indiv_param = NULL
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`tdm`	A boolean. If `TRUE`: estimates the optimal dose for a selected target concentration at a selected point in time following the events from `dat`, and using Maximum A Posteriori estimation. Setting `tdm` to `TRUE` causes the following to occur: the arguments `estim_method`, `p`, `greater_than`, `interdose_interval`, `add_dose`, `indiv_param` and `starting_time` are ignored.
`time_c`	Numeric. Point in time for which the dose is to be optimized.
`time_dose`	Numeric. Time when the dose is to be given.
`target_conc`	Numeric. Target concentration.
`cmt_dose`	Character or numeric. The compartment in which the dose is to be administered. Must match one of the compartments in the prior model. Defaults to 1.
`endpoint`	Character. The endpoint of the prior model to be optimised for. The default is "Cc", which is the central concentration.
`estim_method`	A character string. An estimation method to be used for the individual parameters. The default method "map" is the Maximum A Posteriori estimation, the method "prior" simulates from the prior population model, and "sir" uses the Sequential Importance Resampling algorithm to estimate the a posteriori distribution of the individual parameters. This argument is ignored if `indiv_param` is provided or if `tdm` is set to `TRUE`.
`nocb`	A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.
`p`	Numeric. The proportion of the distribution of concentrations to consider for the optimization. Mandatory for `estim_method=sir`. This argument is ignored if `tdm` is set to `TRUE`.
`greater_than`	A boolean. If `TRUE`: targets a dose leading to a proportion `p` of the concentrations to be greater than `target_conc`. Respectively, lower if `FALSE`. This argument is ignored if `tdm` is set to `TRUE`.
`starting_dose`	Numeric. Starting dose for the optimization algorithm.
`interdose_interval`	Numeric. Time for the interdose interval for multiple dose regimen. Must be provided when add_dose is used. This argument is ignored if `tdm` is set to `TRUE`.
`add_dose`	Numeric. Additional doses administered at inter-dose interval after the first dose. Optional. This argument is ignored if `tdm` is set to `TRUE`.
`duration`	Numeric. Duration of infusion, for zero-order administrations.
`indiv_param`	Optional. A set of individual parameters : THETA, estimates of ETA, and covariates. This argument is ignored if `tdm` is set to `TRUE`.

Value

A list containing the following components:

dose: Numeric. An optimal dose for the selected target concentration.
type_of_estimate: Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
conc_estimate: A vector of numeric estimates of the conc. Either a single value (for a point estimate of ETA), or a distribution.
indiv_param: A data.frame. The set of individual parameters used for the determination of the optimal dose : THETA, estimates of ETA, and covariates

Examples

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the optimal dose to reach a concentration of 80 mg/l
# one hour after starting the 30-minutes infusion
poso_dose_conc(dat=df_patient01,prior_model=mod_run001,
time_c=1,duration=0.5,target_conc=80)

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the optimal dose to reach a concentration of 80 mg/l
# one hour after starting the 30-minutes infusion
poso_dose_conc(dat=df_patient01,prior_model=mod_run001,
time_c=1,duration=0.5,target_conc=80)

Estimate the Maximum A Posteriori individual parameters

Description

Estimates the Maximum A Posteriori (MAP) individual parameters, also known as Empirical Bayes Estimates (EBE).

Usage

poso_estim_map(
  dat = NULL,
  prior_model = NULL,
  return_model = TRUE,
  return_ofv = FALSE,
  nocb = FALSE
)
poso_estim_map(
  dat = NULL,
  prior_model = NULL,
  return_model = TRUE,
  return_ofv = FALSE,
  nocb = FALSE
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`return_model`	A boolean. Returns a rxode2 model using the estimated ETAs if set to `TRUE`.
`return_ofv`	A boolean. Returns a the Objective Function Value (OFV) if set to `TRUE`.
`nocb`	A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.

Value

A named list consisting of one or more of the following elements depending on the input parameters of the function: ⁠$eta⁠ a named vector of the MAP estimates of the individual values of ETA, ⁠$model⁠ an rxode2 model using the estimated ETAs, ⁠$event⁠ the data.table used to solve the returned rxode2 model.

Examples

rxode2::setRxThreads(1) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the Maximum A Posteriori individual parameters
poso_estim_map(dat=df_patient01,prior_model=mod_run001)

rxode2::setRxThreads(1) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the Maximum A Posteriori individual parameters
poso_estim_map(dat=df_patient01,prior_model=mod_run001)

Estimate the posterior distribution of individual parameters by MCMC

Description

Estimates the posterior distribution of individual parameters by Markov Chain Monte Carlo (using a Metropolis-Hastings algorithm)

Usage

poso_estim_mcmc(
  dat = NULL,
  prior_model = NULL,
  return_model = TRUE,
  burn_in = 50,
  n_iter = 1000,
  n_chains = 4,
  nocb = FALSE,
  control = list(n_kernel = c(2, 2, 2), stepsize_rw = 0.4, proba_mcmc = 0.3, nb_max = 3)
)
poso_estim_mcmc(
  dat = NULL,
  prior_model = NULL,
  return_model = TRUE,
  burn_in = 50,
  n_iter = 1000,
  n_chains = 4,
  nocb = FALSE,
  control = list(n_kernel = c(2, 2, 2), stepsize_rw = 0.4, proba_mcmc = 0.3, nb_max = 3)
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`return_model`	A boolean. Returns a rxode2 model using the estimated ETAs if set to `TRUE`.
`burn_in`	Number of burn-in iterations for the Metropolis-Hastings algorithm.
`n_iter`	Total number of iterations (following the burn-in iterations) for each Markov chain of the Metropolis-Hastings algorithm.
`n_chains`	Number of Markov chains
`nocb`	A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.
`control`	A list of parameters controlling the Metropolis-Hastings algorithm.

Value

If return_model is set to FALSE, a list of one element: a dataframe ⁠$eta⁠ of ETAs from the posterior distribution, estimated by Markov Chain Monte Carlo. If return_model is set to TRUE, a list of the dataframe of the posterior distribution of ETA, and a rxode2 model using the estimated distributions of ETAs.

Author(s)

Emmanuelle Comets, Audrey Lavenu, Marc Lavielle, Cyril Leven

References

Comets E, Lavenu A, Lavielle M. Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software 80, 3 (2017), 1-41.

Examples

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the posterior distribution of population parameters
poso_estim_mcmc(dat=df_patient01,prior_model=mod_run001,
n_iter=50,n_chains=2)

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the posterior distribution of population parameters
poso_estim_mcmc(dat=df_patient01,prior_model=mod_run001,
n_iter=50,n_chains=2)

Estimate the posterior distribution of individual parameters by SIR

Description

Estimates the posterior distribution of individual parameters by Sequential Importance Resampling (SIR)

Usage

poso_estim_sir(
  dat = NULL,
  prior_model = NULL,
  n_sample = 10000,
  n_resample = 1000,
  return_model = TRUE,
  nocb = FALSE
)
poso_estim_sir(
  dat = NULL,
  prior_model = NULL,
  n_sample = 10000,
  n_resample = 1000,
  return_model = TRUE,
  nocb = FALSE
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`n_sample`	Number of samples from the S-step
`n_resample`	Number of samples from the R-step
`return_model`	A boolean. Returns a rxode2 model using the estimated ETAs if set to `TRUE`.
`nocb`	A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.

Value

If return_model is set to FALSE, a list of one element: a dataframe ⁠$eta⁠ of ETAs from the posterior distribution, estimated by Sequential Importance Resampling. If return_model is set to TRUE, a list of the dataframe of the posterior distribution of ETA, and a rxode2 model using the estimated distributions of ETAs.

Examples

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the posterior distribution of population parameters
poso_estim_sir(dat=df_patient01,prior_model=mod_run001,
n_sample=1e3,n_resample=1e2)

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the posterior distribution of population parameters
poso_estim_sir(dat=df_patient01,prior_model=mod_run001,
n_sample=1e3,n_resample=1e2)

Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin)

Description

Estimates the optimal dosing interval to consistently achieve a target Cmin, given a dose, a population pharmacokinetic model, a set of individual parameters, and a target concentration.

Usage

poso_inter_cmin(
  dat = NULL,
  prior_model = NULL,
  dose,
  target_cmin,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_interval = 12,
  add_dose = 10,
  duration = 0,
  indiv_param = NULL
)
poso_inter_cmin(
  dat = NULL,
  prior_model = NULL,
  dose,
  target_cmin,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_interval = 12,
  add_dose = 10,
  duration = 0,
  indiv_param = NULL
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`dose`	Numeric. The dose given.
`target_cmin`	Numeric. Target trough concentration (Cmin).
`cmt_dose`	Character or numeric. The compartment in which the dose is to be administered. Must match one of the compartments in the prior model. Defaults to 1.
`endpoint`	Character. The endpoint of the prior model to be optimised for. The default is "Cc", which is the central concentration.
`estim_method`	A character string. An estimation method to be used for the individual parameters. The default method "map" is the Maximum A Posteriori estimation, the method "prior" simulates from the prior population model, and "sir" uses the Sequential Importance Resampling algorithm to estimate the a posteriori distribution of the individual parameters. This argument is ignored if `indiv_param` is provided.
`nocb`	A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.
`p`	Numeric. The proportion of the distribution of concentrations to consider for the optimization. Mandatory for `estim_method=sir`.
`greater_than`	A boolean. If `TRUE`: targets a dose leading to a proportion `p` of the concentrations to be greater than `target_conc`. Respectively, lower if `FALSE`.
`starting_interval`	Numeric. Starting inter-dose interval for the optimization algorithm.
`add_dose`	Numeric. Additional doses administered at inter-dose interval after the first dose.
`duration`	Numeric. Duration of infusion, for zero-order administrations.
`indiv_param`	Optional. A set of individual parameters : THETA, estimates of ETA, and covariates.

Value

A list containing the following components:

interval: Numeric. An inter-dose interval to reach the target trough concentration before each dosing of a multiple dose regimen.
type_of_estimate: Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
conc_estimate: A vector of numeric estimates of the conc. Either a single value (for a point estimate of ETA), or a distribution.
indiv_param: A data.frame. The set of individual parameters used for the determination of the optimal dose : THETA, estimates of ETA, and covariates

Examples

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the optimal interval to reach a cmin of of 2.5 mg/l
# before each administration
poso_inter_cmin(dat=df_patient01,prior_model=mod_run001,
dose=1500,duration=0.5,target_cmin=2.5)

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the optimal interval to reach a cmin of of 2.5 mg/l
# before each administration
poso_inter_cmin(dat=df_patient01,prior_model=mod_run001,
dose=1500,duration=0.5,target_cmin=2.5)

Update a model with events from a new rxode2 event table

Description

Update a model with events from a new rxode2 event table, while accounting for and interpolating any covariates or inter-occasion variability.

Usage

poso_replace_et(
  target_model = NULL,
  prior_model = NULL,
  event_table = NULL,
  interpolation = "locf"
)
poso_replace_et(
  target_model = NULL,
  prior_model = NULL,
  event_table = NULL,
  interpolation = "locf"
)

Arguments

`target_model`	Solved rxode2 object. A model generated by one of posologyr's estimation functions.
`prior_model`	A `posologyr` prior population model.
`event_table`	An rxode2 event table.
`interpolation`	Character string. Specifies the interpolation method to be used for covariates. Choices are "locf" for last observation carried forward, "nocb" for next observation carried backward, "midpoint", or "linear".

Value

A solved rxode2 object, updated with the event table provided.

Examples

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the prior distribution of population parameters
pop_model <- poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100)
# create a new rxode2 event table from the initial dataset
new_et <- rxode2::as.et(df_patient01)
new_et$add_sampling(seq(14,15,by=0.1))
# update the model with the new event table
poso_replace_et(pop_model$model,mod_run001,event_table=new_et)

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the prior distribution of population parameters
pop_model <- poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100)
# create a new rxode2 event table from the initial dataset
new_et <- rxode2::as.et(df_patient01)
new_et$add_sampling(seq(14,15,by=0.1))
# update the model with the new event table
poso_replace_et(pop_model$model,mod_run001,event_table=new_et)

Estimate the prior distribution of population parameters

Description

Estimates the prior distribution of population parameters by Monte Carlo simulations

Usage

poso_simu_pop(
  dat = NULL,
  prior_model = NULL,
  n_simul = 1000,
  return_model = TRUE
)
poso_simu_pop(
  dat = NULL,
  prior_model = NULL,
  n_simul = 1000,
  return_model = TRUE
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`n_simul`	An integer, the number of simulations to be run. For `n_simul =0`, all ETAs are set to 0.
`return_model`	A boolean. Returns a rxode2 model using the simulated ETAs if set to `TRUE`.

Value

If return_model is set to FALSE, a list of one element: a dataframe ⁠$eta⁠ of the individual values of ETA. If return_model is set to TRUE, a list of the dataframe of the individual values of ETA, and a rxode2 model using the simulated ETAs.

Examples

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the prior distribution of population parameters
poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100)

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the prior distribution of population parameters
poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100)

Estimate the time required to reach a target trough concentration (Cmin)

Description

Estimates the time required to reach a target trough concentration (Cmin) given a population pharmacokinetic model, a set of individual parameters, a dose, and a target Cmin.

Usage

poso_time_cmin(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  target_cmin,
  dose = NULL,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  from = 0.2,
  last_time = 72,
  add_dose = NULL,
  interdose_interval = NULL,
  duration = 0,
  indiv_param = NULL
)
poso_time_cmin(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  target_cmin,
  dose = NULL,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  from = 0.2,
  last_time = 72,
  add_dose = NULL,
  interdose_interval = NULL,
  duration = 0,
  indiv_param = NULL
)

Arguments

`dat`	Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
`prior_model`	A `posologyr` prior population pharmacokinetics model, a list of six objects.
`tdm`	A boolean. If `TRUE`: computes the predicted time to reach the target trough concentration (Cmin) following the last event from `dat`, and using Maximum A Posteriori estimation. Setting `tdm` to `TRUE` causes the following to occur: the simulation starts at the time of the last recorded dose (from the TDM data) plus `from`; the simulation stops at the time of the last recorded dose (from the TDM data) plus `last_time`; the arguments `dose`, `duration`, `estim_method`, `p`, `greater_than`, `interdose_interval`, `add_dose`, `indiv_param` and `starting_time` are ignored.
`target_cmin`	Numeric. Target trough concentration (Cmin).
`dose`	Numeric. Dose administered. This argument is ignored if `tdm` is set to `TRUE`.
`cmt_dose`	Character or numeric. The compartment in which the dose is to be administered. Must match one of the compartments in the prior model. Defaults to 1.
`endpoint`	Character. The endpoint of the prior model to be optimised for. The default is "Cc", which is the central concentration.
`estim_method`	A character string. An estimation method to be used for the individual parameters. The default method "map" is the Maximum A Posteriori estimation, the method "prior" simulates from the prior population model, and "sir" uses the Sequential Importance Resampling algorithm to estimate the a posteriori distribution of the individual parameters. This argument is ignored if `indiv_param` is provided, or if `tdm` is set to `TRUE`.
`nocb`	A boolean. For time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If `FALSE`, the last observation carried forward (locf) style will be used. Defaults to `FALSE`.
`p`	Numeric. The proportion of the distribution of Cmin to consider for the estimation. Mandatory for `estim_method=sir`. This argument is ignored if `tdm` is set to `TRUE`.
`greater_than`	A boolean. If `TRUE`: targets a time leading to a proportion `p` of the cmins to be greater than `target_cmin`. Respectively, lower if `FALSE`. This argument is ignored if `tdm` is set to `TRUE`.
`from`	Numeric. Starting time for the simulation of the individual time-concentration profile. The default value is 0.2. When `tdm` is set to `TRUE` the simulation starts at the time of the last recorded dose plus `from`.
`last_time`	Numeric. Ending time for the simulation of the individual time-concentration profile. The default value is 72. When `tdm` is set to `TRUE` the simulation stops at the time of the last recorded dose plus `last_time`.
`add_dose`	Numeric. Additional doses administered at inter-dose interval after the first dose. Optional. This argument is ignored if `tdm` is set to `TRUE`.
`interdose_interval`	Numeric. Time for the inter-dose interval for multiple dose regimen. Must be provided when add_dose is used. This argument is ignored if `tdm` is set to `TRUE`.
`duration`	Numeric. Duration of infusion, for zero-order administrations. This argument is ignored if `tdm` is set to `TRUE`.
`indiv_param`	Optional. A set of individual parameters : THETA, estimates of ETA, and covariates.

Value

A list containing the following components:

time: Numeric. Time needed to reach the selected Cmin.
type_of_estimate: Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
cmin_estimate: A vector of numeric estimates of the Cmin. Either a single value (for a point estimate of ETA), or a distribution.
indiv_param: A data.frame. The set of individual parameters used for the determination of the time needed to reach a selected Cmin: THETA, estimates of ETA, and covariates

Examples

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# predict the time needed to reach a concentration of 2.5 mg/l
# after the administration of a 2500 mg dose over a 30 minutes
# infusion
poso_time_cmin(dat=df_patient01,prior_model=mod_run001,
dose=2500,duration=0.5,from=0.5,target_cmin=2.5)

rxode2::setRxThreads(2L) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# predict the time needed to reach a concentration of 2.5 mg/l
# after the administration of a 2500 mg dose over a 30 minutes
# infusion
poso_time_cmin(dat=df_patient01,prior_model=mod_run001,
dose=2500,duration=0.5,from=0.5,target_cmin=2.5)

Package 'posologyr'

Help Index

Estimate the dose needed to reach a target area under the concentration-time curve (AUC)

Description

Usage

Arguments

Value

Examples

Estimate the optimal dose to achieve a target concentration at any given time

Description

Usage

Arguments

Value

Examples

Estimate the Maximum A Posteriori individual parameters

Description

Usage

Arguments

Value

Examples

Estimate the posterior distribution of individual parameters by MCMC

Description

Usage

Arguments

Value

Author(s)

References

Examples

Estimate the posterior distribution of individual parameters by SIR

Description

Usage

Arguments

Value

Examples

Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin)

Description

Usage

Arguments

Value

Examples

Update a model with events from a new rxode2 event table

Description

Usage

Arguments

Value

Examples

Estimate the prior distribution of population parameters

Description

Usage

Arguments

Value

Examples

Estimate the time required to reach a target trough concentration (Cmin)

Description

Usage

Arguments

Value

Examples