Type:	Package
Title:	Pharmacometric and Pharmacokinetic Toolkit
Version:	1.3
Maintainer:	Justin Wilkins <justin.wilkins@occams.com>
Contact:	Justin Wilkins <justin.wilkins@occams.com>
Description:	Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
License:	GPL-2
URL:	https://github.com/kestrel99/pmxTools
BugReports:	https://github.com/kestrel99/pmxTools/issues
RoxygenNote:	7.2.3
Imports:	ggplot2, chron, xml2, dplyr (≥ 0.8.5), tibble, gghalves, ggdist, scales, MASS, stringr, PKNCA, magrittr, data.tree
Depends:	R (≥ 3.50), patchwork
Suggests:	testthat, vdiffr, knitr, rmarkdown
Encoding:	UTF-8
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2023-02-21 15:42:50 UTC; justin
Author:	Justin Wilkins [aut, cre], Bill Denney [aut], Rik Schoemaker [aut], Satyaprakash Nayak [ctb], Leonid Gibiansky [ctb], Andrew Hooker [ctb], E. Niclas Jonsson [ctb], Mats O. Karlsson [ctb], John Johnson [ctb]
Repository:	CRAN
Date/Publication:	2023-02-21 16:00:08 UTC

A transform for ggplot2 with data that may be below the lower limit of quantification

Description

If the lloq is not provided, it will be estimated from the data as the minimum value above zero.

Usage

blq_trans(lloq, x, multiplier = 0.5, lloq_text)

blq_log_trans(lloq, x, multiplier = 0.5, base = 10, lloq_text)

Arguments

Value

A "trans" object based on the scales package for BLQ data.

Functions

• blq_log_trans(): Log-scale transformation with BLQ

See Also

Other BLQ Transformation: breaks_blq_general(), estimate_lloq(), ftrans_blq_linear(), itrans_blq_linear(), label_blq()

Examples

## Not run: 
library(ggplot2)
ggplot(data=data.frame(x=1:10, y=1:10), aes(x=x, y=y)) +
  geom_point()

ggplot(data=data.frame(x=1:10, y=1:10), aes(x=x, y=y)) +
  geom_point() +
  scale_x_continuous(trans=blq_trans(lloq=3))

ggplot(data=data.frame(x=1:10, y=1:10), aes(x=x, y=y)) +
  geom_point() +
  scale_x_continuous(trans=blq_log10_trans(lloq=3))

## End(Not run)

Generate breaks for measurements below the limit of quantification

Description

Breaks that are < lloq are removed. If the lowest break is removed if it is too close to the lloq.

Usage

breaks_blq_general(lloq, breakfun, trans = identity, ...)

Arguments

Details

For ggplot2 scales. This is not usually used directly. See blq_trans() and blq_log10_trans() for the functions that are more commonly used.

Value

A function for calculating breaks with arguments x and n

See Also

Other BLQ Transformation: blq_trans(), estimate_lloq(), ftrans_blq_linear(), itrans_blq_linear(), label_blq()

Examples

breaks_blq_general(lloq=3, breakfun=scales::breaks_extended)(1:100, n=5)

Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.

Description

Calculate derived pharmacokinetic parameters for a 1-, 2-, or 3-compartment linear model.

Usage

calc_derived(..., verbose = FALSE)

calc_derived_1cpt(
  CL,
  V = NULL,
  V1 = NULL,
  ka = NULL,
  dur = NULL,
  tlag = NULL,
  tinf = NULL,
  dose = NULL,
  tau = NULL,
  step = 0.1,
  type = "all",
  sigdig = 5
)

calc_derived_2cpt(
  CL,
  V1 = NULL,
  V2,
  Q2 = NULL,
  V = NULL,
  Q = NULL,
  dur = NULL,
  tinf = NULL,
  ka = NULL,
  tlag = NULL,
  dose = NULL,
  tau = NULL,
  step = 0.1,
  type = "all",
  sigdig = 5
)

calc_derived_3cpt(
  CL,
  V1 = NULL,
  V2,
  V3,
  Q2 = NULL,
  Q3,
  V = NULL,
  Q = NULL,
  ka = NULL,
  dur = NULL,
  tinf = NULL,
  tlag = NULL,
  dose = NULL,
  tau = NULL,
  step = 0.1,
  type = "all",
  sigdig = 5
)

Arguments

Value

Return a list of derived PK parameters for a 1-, 2-, or 3-compartment linear model given provided clearances and volumes based on the type. If a dose is provided, estimated non-compartmental analysis (NCA) parameters will be provided as well, based on simulation of single-dose and (if 'tau' is specified) steady-state time courses.

• Vss: Volume of distribution at steady state, V_{ss} (volume units, e.g. L); 1-, 2-, and 3-compartment

• k10: First-order elimination rate, k_{10} (inverse time units, e.g. 1/h); 1-, 2-, and 3-compartment

• k12: First-order rate of transfer from central to first peripheral compartment, k_{12} (inverse time units, e.g. 1/h); 2- and 3-compartment

• k21: First-order rate of transfer from first peripheral to central compartment, k_{21} (inverse time units, e.g. 1/h); 2- and 3-compartment

• k13: First-order rate of transfer from central to second peripheral compartment, k_{13} (inverse time units, e.g. 1/h); 3-compartment

• k31: First-order rate of transfer from second peripheral to central compartment,k_{31} (inverse time units, e.g. 1/h); 3-compartment

• thalf_alpha: t_{1/2,\alpha} (time units, e.g. h); 1-, 2-, and 3-compartment

• thalf_beta: t_{1/2,\beta} (time units, e.g. h); 2- and 3-compartment

• thalf_gamma: t_{1/2,\gamma} (time units, e.g. h); 3-compartment

• alpha: \alpha; 1-, 2-, and 3-compartment

• beta: \beta; 2- and 3-compartment

• gamma: \beta; 3-compartment

• trueA: true A; 1-, 2-, and 3-compartment

• trueB: true B; 2- and 3-compartment

• trueC: true C; 3-compartment

• fracA: fractional A; 1-, 2-, and 3-compartment

• fracB: fractional B; 2- and 3-compartment

• fracC: fractional C; 3-compartment

• AUCinf: Area under the concentration-time curve to infinity (single dose)

• AUCtau: Area under the concentration-time curve over the dosing interval at steady state

• Cmax: Maximum concentration after a single dose

• Cmaxss: Maximum concentration over the dosing interval at steady state

• Tmax: Time after dose of maximum concentration

• AUCinf_dose_normalized: Dose-normalized area under the concentration-time curve to infinity (single dose)

• AUCtau_dose_normalized: Dose-normalized area under the concentration-time curve over the dosing interval at steady state

• Cmax_dose_normalized: Dose-normalized maximum concentration after a single dose

• Cmaxss_dose_normalized: Dose-normalized maximum concentration over the dosing interval at steady state

• step: Time increment used when estimating NCA parameters.

The input parameters with standardized names (dose, V1, V2, V3, CL, Q2, and Q3) are also returned in the list, and if provided, additional PK parameters of 'ka', 'tlag', 'tinf' and 'dur' are also returned in the list. All inputs may be scalars or vectors.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Bill Denney, wdenney@humanpredictions.com

References

Shafer S. L. CONVERT.XLS

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

params <- calc_derived(CL=29.4, V1=23.4, V2=114, V3=4614, Q2=270, Q3=73)
params <- calc_derived_1cpt(CL=16, V=25)
params <- calc_derived_2cpt(CL=16, V1=25, V2=50, Q=0.5)
params <- calc_derived_3cpt(CL=29.4, V1=23.4, V2=114, V3=4614, Q2=270, Q3=73)

Calculate C(t) for a 1-compartment linear model

Description

Calculate C(t) for a 1-compartment linear model

Usage

calc_sd_1cmt(t, dose, dur = NULL, tinf = NULL, ...)

calc_sd_1cmt_linear_bolus(t, dose, ...)

calc_sd_1cmt_linear_oral_1_lag(t, dose, ...)

calc_sd_1cmt_linear_infusion(t, dose, tinf, ...)

calc_sd_1cmt_linear_oral_0(t, dose, dur, ...)

calc_sd_1cmt_linear_oral_1(t, dose, ...)

calc_sd_1cmt_linear_oral_0_lag(t, dose, dur, ...)

Arguments

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_sd_1cmt_linear_bolus(): Calculate C(t) for a 1-compartment linear model after a single IV bolus dose

• calc_sd_1cmt_linear_oral_1_lag(): Calculate C(t) for a 1-compartment linear model with first-order absorption after a single oral dose, with lag time

• calc_sd_1cmt_linear_infusion(): Calculate C(t) for a 1-compartment linear model after a single IV infusion

• calc_sd_1cmt_linear_oral_0(): Calculate C(t) for a 1-compartment linear model with zero-order absorption after a single oral dose

• calc_sd_1cmt_linear_oral_1(): Calculate C(t) for a 1-compartment linear model with first-order absorption after a single oral dose

• calc_sd_1cmt_linear_oral_0_lag(): Calculate C(t) for a 1-compartment linear model with zero-order absorption after a single oral dose, with lag time

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Bill Denney, wdenney@humanpredictions.com

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_sd_1cmt_linear_bolus(t=0:24, CL=6, V=25, dose=600)
Ct <- calc_sd_1cmt_linear_oral_1_lag(t=0:24, CL=6, V=25, ka=1.1, dose=600, tlag=2)
Ct <- calc_sd_1cmt_linear_infusion(t=0:24, CL=6, V=25, dose=600, tinf=1)
Ct <- calc_sd_1cmt_linear_oral_0(t=0:24, CL=6, V=25, dur=1.5, dose=600)
Ct <- calc_sd_1cmt_linear_oral_1(t=0:24, CL=6, V=25, ka=1.1, dose=600)
Ct <- calc_sd_1cmt_linear_oral_0_lag(t=0:24, CL=6, V=25, dur=1.5, dose=600, tlag=1.5)

Calculate C(t) for a 2-compartment linear model

Description

Calculate C(t) for a 2-compartment linear model

Usage

calc_sd_2cmt(t, dose, dur = NULL, tinf = NULL, ...)

calc_sd_2cmt_linear_bolus(t, dose, ...)

calc_sd_2cmt_linear_oral_1_lag(t, dose, ...)

calc_sd_2cmt_linear_infusion(t, dose, tinf, ...)

calc_sd_2cmt_linear_oral_0_lag(t, dose, dur, ...)

calc_sd_2cmt_linear_oral_1(t, dose, ...)

calc_sd_2cmt_linear_oral_0(t, dose, dur, ...)

Arguments

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_sd_2cmt_linear_bolus(): Calculate C(t) for a 2-compartment linear model after a single IV bolus dose

• calc_sd_2cmt_linear_oral_1_lag(): Calculate C(t) for a 2-compartment linear model after a single first-order oral dose with a lag time

• calc_sd_2cmt_linear_infusion(): Calculate C(t) for a 2-compartment linear model after a single infusion

• calc_sd_2cmt_linear_oral_0_lag(): Calculate C(t) for a 2-compartment linear model after a single zero-order oral dose, with lag time

• calc_sd_2cmt_linear_oral_1(): Calculate C(t) for a 2-compartment linear model after a single first-order oral dose

• calc_sd_2cmt_linear_oral_0(): Calculate C(t) for a 2-compartment linear model after a single zero-order oral dose

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Bill Denney, wdenney@humanpredictions.com

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_sd_2cmt_linear_bolus(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5, dose = 10)
Ct <- calc_sd_2cmt_linear_oral_1_lag(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1, tlag = 2)
Ctrough <- calc_sd_2cmt_linear_infusion(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 10, tinf = 1)
Ctrough <- calc_sd_2cmt_linear_oral_0_lag(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1, tlag=2)
Ct <- calc_sd_2cmt_linear_oral_1(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1)
Ct <- calc_sd_2cmt_linear_oral_0(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1)

Calculate C(t) for a 3-compartment linear model

Description

Calculate C(t) for a 3-compartment linear model

Usage

calc_sd_3cmt(t, dose, dur = NULL, tinf = NULL, ...)

calc_sd_3cmt_linear_bolus(t, dose, ...)

calc_sd_3cmt_linear_oral_1_lag(t, dose, ...)

calc_sd_3cmt_linear_infusion(t, dose, tinf, ...)

calc_sd_3cmt_linear_oral_0(t, dose, dur, ...)

calc_sd_3cmt_linear_oral_0_lag(t, dose, dur, ...)

calc_sd_3cmt_linear_oral_1(t, dose, ...)

Arguments

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_sd_3cmt_linear_bolus(): Calculate C(t) for a 3-compartment linear model after a single IV bolus dose

• calc_sd_3cmt_linear_oral_1_lag(): Calculate C(t) for a 3-compartment linear model after a single oral dose

• calc_sd_3cmt_linear_infusion(): Calculate C(t) for a 3-compartment linear model after a single IV infusion

• calc_sd_3cmt_linear_oral_0(): Calculate C(t) for a 3-compartment linear model after a single dose, with zero-order absorption

• calc_sd_3cmt_linear_oral_0_lag(): Calculate C(t) for a 3-compartment linear model after a single dose, with zero-order absorption and a lag time

• calc_sd_3cmt_linear_oral_1(): Calculate C(t) for a 3-compartment linear model after a single oral dose

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Bill Denney, wdenney@humanpredictions.com

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_sd_3cmt_linear_bolus(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dose = 100)
Ct <- calc_sd_3cmt_linear_oral_1_lag(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100, tlag = 1.5)
Ct <- calc_sd_3cmt_linear_infusion(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dose = 100, tinf=1)
Ct <- calc_sd_3cmt_linear_oral_0(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100)
Ct <- calc_sd_3cmt_linear_oral_0_lag(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100, tlag=1.5)
Ct <- calc_sd_3cmt_linear_oral_1(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100)

Calculate C(t) for a 1-compartment linear model at steady-state

Description

Calculate C(t) for a 1-compartment linear model at steady-state

Usage

calc_ss_1cmt(tad, tau, dose, dur = NULL, tinf = NULL, ...)

calc_ss_1cmt_linear_bolus(tad, tau, dose, ...)

calc_ss_1cmt_linear_infusion(tad, tau, dose, tinf, ...)

calc_ss_1cmt_linear_oral_0(tad, tau, dose, dur, ...)

calc_ss_1cmt_linear_oral_0_lag(tad, tau, dose, dur, ...)

calc_ss_1cmt_linear_oral_1_lag(tad, tau, dose, ...)

calc_ss_1cmt_linear_oral_1(tad, tau, dose, ...)

Arguments

Value

Concentration of drug at requested time (t) after a single dose, given provided set of parameters and variables.

Functions

• calc_ss_1cmt_linear_bolus(): Calculate C(t) for a 1-compartment linear model with IV bolus dosing at steady state

• calc_ss_1cmt_linear_infusion(): Calculate C(t) for a 1-compartment linear model with infusion at steady state

• calc_ss_1cmt_linear_oral_0(): Calculate C(t) for a 1-compartment linear model with zero-order oral absorption at steady state

• calc_ss_1cmt_linear_oral_0_lag(): Calculate C(t) for a 1-compartment linear model with zero-order oral absorption at steady state, with lag time

• calc_ss_1cmt_linear_oral_1_lag(): Calculate C(t) for a 1-compartment linear model with first-order oral absorption at steady state, with lag time

• calc_ss_1cmt_linear_oral_1(): Calculate C(t) for a 1-compartment linear model with first-order oral absorption at steady state

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Bill Denney, wdenney@humanpredictions.com

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_ss_1cmt_linear_bolus(t=0:24, CL=6, V=25, dose=600, tau=24)
Ct <- calc_ss_1cmt_linear_infusion(tad=0:36, CL=2, V=25, dose=600, tinf=1, tau=24)
Ct <- calc_ss_1cmt_linear_oral_0(tad=0:36, CL=2, V=25, dose=600, dur=1, tau=24)
Ct <- calc_ss_1cmt_linear_oral_0_lag(tad=0:36, CL=2, V=25, dose=600, dur=1, tau=24, tlag=1.5)
Ct <- calc_ss_1cmt_linear_oral_1_lag(tad=0:36, CL=2, V=25, dose=600,
    ka=0.25, tlag=0.75, tau=24)
Ct <- calc_ss_1cmt_linear_oral_1(tad=0:36, CL=2, V=25, dose=600, ka=0.25, tau=24)

Calculate C(t) for a 2-compartment linear model at steady-state

Description

Calculate C(t) for a 2-compartment linear model at steady-state

Usage

calc_ss_2cmt(tad, tau, dose, dur = NULL, tinf = NULL, ...)

calc_ss_2cmt_linear_bolus(tad, tau, dose, ...)

calc_ss_2cmt_linear_infusion(tad, tau, dose, tinf, ...)

calc_ss_2cmt_linear_oral_0(tad, tau, dose, dur, ...)

calc_ss_2cmt_linear_oral_1_lag(tad, tau, dose, ...)

calc_ss_2cmt_linear_oral_0_lag(tad, tau, dose, dur, ...)

calc_ss_2cmt_linear_oral_1(tad, tau, dose, ...)

Arguments

Value

Concentration of drug at requested time (t) at steady-state, given provided set of parameters and variables.

Functions

• calc_ss_2cmt_linear_bolus(): Calculate C(t) for a 2-compartment linear model with IV bolus dosing at steady-state

• calc_ss_2cmt_linear_infusion(): Calculate C(t) for a 2-compartment linear model with infusion at steady state

• calc_ss_2cmt_linear_oral_0(): Calculate C(t) for a 2-compartment linear model at steady-state with zero-order oral dosing

• calc_ss_2cmt_linear_oral_1_lag(): Calculate C(t) for a 2-compartment linear model at steady-state with first-order oral dosing

• calc_ss_2cmt_linear_oral_0_lag(): Calculate C(t) for a 2-compartment linear model at steady-state with zero-order oral dosing and a lag time

• calc_ss_2cmt_linear_oral_1(): Calculate C(t) for a 2-compartment linear model at steady-state with first-order oral dosing

Author(s)

Justin Wilkins, justin.wilkins@occams.com

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_ss_2cmt_linear_bolus(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 10, tau=24)
Ct <- calc_ss_2cmt_linear_infusion(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 10, tinf = 1, tau = 12)
Ct <- calc_ss_2cmt_linear_oral_0(tad = 23, CL = 2.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1, tau = 24)
Ct <- calc_ss_2cmt_linear_oral_1_lag(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1, tau=24, tlag=2)
Ct <- calc_ss_2cmt_linear_oral_0_lag(tad = 23, CL = 2.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, dur = 1, tau = 24, tlag=2)
Ct <- calc_ss_2cmt_linear_oral_1(tad = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
    dose = 1000, ka = 1, tau=24)

Calculate C(t) for a 3-compartment linear model at steady-state

Description

Calculate C(t) for a 3-compartment linear model at steady-state

Usage

calc_ss_3cmt(tad, tau, dose, dur = NULL, tinf = NULL, ...)

calc_ss_3cmt_linear_bolus(tad, tau, dose, ...)

calc_ss_3cmt_linear_oral_1_lag(tad, tau, dose, ...)

calc_ss_3cmt_linear_infusion(tad, tau, dose, tinf, ...)

calc_ss_3cmt_linear_oral_0(tad, tau, dose, dur, ...)

calc_ss_3cmt_linear_oral_0_lag(tad, tau, dose, dur, ...)

calc_ss_3cmt_linear_oral_1(tad, tau, dose, ...)

Arguments

Value

Concentration of drug at requested time (t) at steady-state, given provided set of parameters and variables.

Functions

• calc_ss_3cmt_linear_bolus(): Calculate C(t) for a 3-compartment linear model at steady state with IV bolus dosing

• calc_ss_3cmt_linear_oral_1_lag(): Calculate C(t) for a 3-compartment linear model at steady-state with first-order oral dosing with a lag time

• calc_ss_3cmt_linear_infusion(): Calculate C(t) for a 3-compartment linear model at steady state with IV infusions

• calc_ss_3cmt_linear_oral_0(): Calculate C(t) for a 3-compartment linear model at steady state, with zero-order absorption

• calc_ss_3cmt_linear_oral_0_lag(): Calculate C(t) for a 3-compartment linear model at steady state, with zero-order absorption and lag time

• calc_ss_3cmt_linear_oral_1(): Calculate C(t) for a 3-compartment linear model at steady-state with first-order oral dosing

Author(s)

Justin Wilkins, justin.wilkins@occams.com

References

Bertrand J & Mentre F (2008). Mathematical Expressions of the Pharmacokinetic and Pharmacodynamic Models implemented in the Monolix software. http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Lippincott Williams & Wilkins, Philadelphia, 2010.

Examples

Ct <- calc_ss_3cmt_linear_bolus(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dose = 100, tau=24)
Ctrough <- calc_ss_3cmt_linear_oral_1_lag(t = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100, tau=24, tlag = 1.5)
Ct <- calc_ss_3cmt_linear_infusion(tad = 11.75, CL = 2.5, V1 = 20, V2 = 50,
    V3 = 100, Q2 = 0.5, Q3 = 0.05, dose = 1000, tinf=1, tau=24)
Ct <- calc_ss_3cmt_linear_oral_0(tad = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100, tau = 24)
Ct <- calc_ss_3cmt_linear_oral_0_lag(tad = 11.75, CL = 3.5, V1 = 20, V2 = 500,
    V3 = 200, Q2 = 0.5, Q3 = 0.05, dur = 1, dose = 100, tau = 24, tlag = 1.5)
Ct <- calc_ss_3cmt_linear_oral_1(tad = 11.75, CL = 3.5, V1 = 20,
    V2 = 500, V3 = 200, Q2 = 0.5, Q3 = 0.05, ka = 1, dose = 100, tau = 24)

Count the number of NA values in a vector.

Description

Count the number of NA values in a vector.

Usage

count_na(x)

Arguments

Value

An integer containing the number of NA values in the input vector.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

## Not run: 
 count_na(c(0,5,7,NA,3,3,NA))

## End(Not run)

Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.

Description

Generate a summary table of descriptive data for every individual in a dataset suitable for tabulation in a report.

Usage

dgr_table(
  dat,
  fields,
  names,
  cutoff = 7,
  sig = 3,
  by = NULL,
  idvar = "ID",
  navars = c("-99", "-999")
)

Arguments

Value

A data frame containing a summary of all the fields listed in fields, for each individual in the dataset (the dataset should not contain duplicated individuals), conditioned on the field in by. Continuous values are summarized as median, mean, range and number of missing values. Categorical values are summarized as count and relative percentage.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

## Not run: 
 count_na(c(0,5,7,NA,3,3,NA))

## End(Not run)

Estimate the lower limit of quantification (LLOQ) from a vector

Description

Nonnegative values are considered to be above the LLOQ. NA values are ignored.

Usage

estimate_lloq(x)

Arguments

Value

The lowest, nonzero value from x. If all are NA or zero, 1 is returned, and a warning is issued.

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), ftrans_blq_linear(), itrans_blq_linear(), label_blq()

Examples

estimate_lloq(c(NA, 0, 2, 5))

Format a number with the correct number of significant digits and trailing zeroes.

Description

Format a number with the correct number of significant digits and trailing zeroes.

Usage

fmt_signif(x, digits = 3)

Arguments

Value

A string containing the properly-formatted number.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

## Not run: 
 fmt_signif(c(36.44, 0.0002, 3336.7), digits=3)

## End(Not run)

Forward transformation for linear BLQ data

Description

For ggplot2 scales.

Usage

ftrans_blq_linear(lloq, multiplier)

ftrans_blq_log(lloq, multiplier, base = 10)

Arguments

Value

A function of x that replaces x < lloq with lloq*multiplier

Functions

• ftrans_blq_log(): Log-scale transformation

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), estimate_lloq(), itrans_blq_linear(), label_blq()

Calculate a geometric coefficient of variation.

Description

Calculate a geometric coefficient of variation.

Usage

gcv(x, na.rm = F, neg.rm = F)

Arguments

Value

The geometric coefficient of variation of the input vector. If neg.rm is FALSE and values <= 0 are present, NA will be returned.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

## Not run: 
 gcv(myvector)

## End(Not run)

Convert geometric variance or standard deviation to a geometric coefficient of variation

Description

The equation used is: 100*sqrt(exp(gvar)-1)

Usage

gcv_convert(gvar = gsd^2, gsd)

Arguments

Value

Geometric coefficient of variation

Author(s)

Bill Denney

References

http://onbiostatistics.blogspot.com/2008/07/geometric-statistics-geometric-cv-vs.html

Examples

gcv_convert(0.2)
gcv_convert(gsd=0.2)

Calculate the area under the curve (AUC) for each subject over the time interval for dependent variables (dv) using the trapezoidal rule.

Description

Calculate the area under the curve (AUC) for each subject over the time interval for dependent variables (dv) using the trapezoidal rule.

Usage

get_auc(data, time = "TIME", id = "ID", dv = "DV")

Arguments

Value

A data frame containing one AUC value for every subject as defined by id.

Based on the AUC function originally written by Leonid Gibiansky in package MIfuns 5.1, from Metrum Institute.

Author(s)

Leonid Gibiansky, lgibiansky@quantpharm.com

References

https://code.google.com/archive/p/mifuns/

Examples

## Not run: 
 AUCs <- get_auc(myAUCdata)

## End(Not run)

Create a table of model parameter estimates from a NONMEM output object.

Description

Create a table of model parameter estimates from a NONMEM output object.

Usage

get_est_table(
  x,
  thetaLabels = c(),
  omegaLabels = c(),
  sigmaLabels = c(),
  sigdig = 3
)

Arguments

Value

A named vector of NONMEM model parameter estimates.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 estTab   <- get_est_table(nmOutput)

## End(Not run)

Extract variability parameter estimates from a NONMEM output object.

Description

Extract variability parameter estimates from a NONMEM output object.

Usage

get_omega(x, output = "est", sigdig = 6, sep = "-", est.step = NULL)

Arguments

Value

A symmetrical matrix, or a list of symmetrical matrices if all is specified.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

est returns the estimated OMEGA variance-covariance matrix. se returns the standard errors for the estimated OMEGA variance-covariance matrix. rse returns the relative standard errors for the estimated OMEGA variance-covariance matrix (se/est*100). cor returns the correlation matrix matrix. cse returns the standard errors for the correlation matrix. 95ci returns the asymptotic 95% confidence intervals for the elements of the OMEGA variance-covariance matrix (est +/- 1.96*se). all returns all available OMEGA matrices.

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput  <- read_nm("run315.xml")
 omegas    <- get_omega(nmOutput)
 omegaRSEs <- get_omega(nmOutput, "rse")

## End(Not run)

Extract problem and estimation information from a NONMEM output object.

Description

Extract problem and estimation information from a NONMEM output object.

Usage

get_probinfo(x, sigdig = 6, est.step = NULL)

Arguments

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 probInfo <- get_probinfo(nmOutput)

## End(Not run)

Extract shrinkage estimates from a NONMEM output object.

Description

Extract shrinkage estimates from a NONMEM output object.

Usage

get_shrinkage(x, output = "eta", type = "sd", sigdig = 3, est.step = NULL)

Arguments

Value

A named vector of NONMEM shrinkage estimates, or in the case of all, a list of named vectors.

eta returns a vector of ETA shrinkages, as reported by NONMEM. epsilon returns EPSILON shrinkage, as reported by NONMEM. all returns both ETA and EPSILON shrinkage estimates as a list of vectors.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 shr <- get_shrinkage(nmOutput, output="all")

## End(Not run)

Extract residual variability parameter estimates from a NONMEM output object.

Description

Extract residual variability parameter estimates from a NONMEM output object.

Usage

get_sigma(x, output = "est", sigdig = 6, sep = "-", est.step = NULL)

Arguments

Value

A symmetrical matrix, or a list of symmetrical matrices if all is specified.

est returns the estimated SIGMA variance-covariance matrix. se returns the standard errors for the estimated SIGMA variance-covariance matrix. rse returns the relative standard errors for the estimated SIGMA variance-covariance matrix (se/est*100). cor returns the correlation matrix matrix. cse returns the standard errors for the correlation matrix. 95ci returns the asymptotic 95% confidence intervals for the elements of the SIGMA variance-covariance matrix (est +/- 1.96*se). all returns all available SIGMA matrices.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 sigmas   <- get_sigma(nmOutput)
 sigmaRSEs <- get_sigma(nmOutput, "rse")

## End(Not run)

Extract structural model parameter estimates and associated information from a NONMEM output object.

Description

Extract structural model parameter estimates and associated information from a NONMEM output object.

Usage

get_theta(x, output = "est", sigdig = 6, sep = "-", est.step = NULL)

Arguments

Value

A named vector of NONMEM model parameter estimates, or in the case of all, a list of named vectors.

est returns a vector of THETA values. se returns a vector of THETA standard errors. rse returns a vector of THETA relative standard errors (se/est*100). 95ci returns a vector of the asymptotic 95% confidence intervals for the elements of THETA (est +/- 1.96*se). all returns all available THETA information as a list of named vectors.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmOutput <- read_nm("run315.xml")
 thetas <- get_theta(nmOutput)

## End(Not run)

Calculate geometric mean

Description

Calculate geometric mean

Usage

gm(x)

Arguments

Value

The geometric mean. NA is returned if there are any non-positive elements in x.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

gm(c(0.5, 7, 8, 5))

Inverse transformation for linear BLQ data

Description

For ggplot2 scales.

Usage

itrans_blq_linear(lloq)

itrans_blq_log(lloq, base)

Arguments

Value

A function of x that replaces x < lloq with lloq

Functions

• itrans_blq_log(): Log-scale inverse transform

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), estimate_lloq(), ftrans_blq_linear(), label_blq()

Label axes with censoring labels for BLQ

Description

For ggplot2 scales.

Usage

label_blq(lloq, lloq_text)

Arguments

Value

A function of x which returns the formatted values.

See Also

Other BLQ Transformation: blq_trans(), breaks_blq_general(), estimate_lloq(), ftrans_blq_linear(), itrans_blq_linear()

Calculate percentage coefficient of variation

Description

Calculate percentage coefficient of variation

Usage

pcv(x, na.rm = FALSE)

Arguments

Value

The percentage coefficient of variation.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

pcv(rnorm(50, 5, 7.56))

Provide concentration-time curves.

Description

Provide concentration-time curves.

Usage

pk_curve(
  t,
  model = "1cmt_oral",
  params = list(ka = 2.77, CL = 2.5, V = 25),
  dose = 600,
  ii = 24,
  addl = 0,
  ss = F
)

Arguments

Value

A data frame containing times (t) and concentrations (cp).

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

plot(pk_curve(t=seq(0,72,by=0.1), model="3cmt_oral", ii=12, addl=5,
  params=list(CL=2.5, V1=25, V2=2, V3=5, Q2=0.5, Q3=0.25, ka=1)), type="l")

Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.

Description

Plot a distribution as a hybrid containing a halfeye, a boxplot and jittered points.

Usage

plot_dist(
  dat,
  yvar,
  xvar = NULL,
  ylim = NULL,
  xlb = "",
  ylb = "",
  identity_line = FALSE,
  identity_value = 0,
  he_adjust = 0.5,
  he_width = 0.6,
  he_justification = -0.2,
  he_col = "black",
  he_fill = "#F8766D",
  he_alpha = 0.9,
  he_slab_type = "pdf",
  he_breaks = "Sturges",
  he_outline_bars = FALSE,
  he_point_interval = "median_qi",
  bxp_width = 0.12,
  bxp_outlier_col = NA,
  bxp_outlier_fill = NA,
  bxp_outlier_shape = 19,
  bxp_outlier_size = 1.5,
  bxp_col = "black",
  bxp_fill = "#F8766D",
  bxp_alpha = 0.9,
  bxp_notch = FALSE,
  bxp_notchwidth = 0.5,
  hp_range_scale = 0.4,
  hp_alpha = 0.25,
  hp_col = "#F8766D",
  hp_transformation = position_jitter(),
  na.rm = FALSE
)

Arguments

Value

A plot containing jittered points, a boxplot and a density plot or histogram illustrating the distribution of every group of the data under evaluation.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

Examples

## Not run: 
 plot_dist(dat, "ETA1", identity_line = T, he_slab_type = "histogram", he_breaks = 30)

## End(Not run)

Plot NONMEM parameter estimation by iteration.

Description

plot_nmprogress returns a plot or set of plots showing the evolution of parameter estimates by iteration.

Usage

plot_nmprogress(
  fileName,
  fileExt = ".lst",
  metric = "perc",
  lineCol = "#902C10",
  idlineCol = "black"
)

Arguments

Value

A set of plots.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
plot_nmprogress("run315")
plot_nmprogress("run315", ".nmlst")

## End(Not run)

Visualize PsN SCM output.

Description

plot_scm returns a visualization of a Perl-speaks-NONMEM (PsN, https://uupharmacometrics.github.io/PsN/) SCM (stepwise covariate modeling) procedure. It depends on the presence of scmlog.txt and short_scmlog.txt files in the specified directory.

Usage

plot_scm(
  dir,
  startPhase = "forward",
  fwdSuccessCol = "#66C2A5",
  fwdFailCol = "black",
  bwdSuccessCol = "#FC8D62",
  bwdFailCol = "black",
  defCol = "black",
  fwdSuccessFillCol = "#B3E2CD",
  fwdFailFillCol = "white",
  bwdSuccessFillCol = "#FDCDAC",
  bwdFailFillCol = "white",
  defFillCol = "white",
  fwdSuccessFontCol = "black",
  fwdFailFontCol = "black",
  bwdSuccessFontCol = "black",
  bwdFailFontCol = "black",
  defFontCol = "black",
  fullFwdCol = "#8DA0CB",
  finalCol = "#E78AC3",
  fullFwdFillCol = "#CBD5E8",
  finalFillCol = "#F4CAE4",
  fullFwdFontCol = "black",
  finalFontCol = "black",
  fullFwdWidth = "2px",
  finalWidth = "2px",
  defWidth = "1px",
  nodeStyle = "filled,rounded",
  nodeShape = "box",
  fontname = "helvetica",
  rankdir = "TB",
  layout = "dot",
  lookupDF = NULL,
  ...
)

Arguments

Details

This function parses PsN SCM output and displays it as a GraphViz graph (effectively, an HTML widget). It is built on plot.Node - please refer to doucmentation for this function for a more detailed overview of what is possible (a lot). For more specific details, see http://rich-iannone.github.io/DiagrammeR/docs.html.

Value

A grViz object.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

GraphViz (https://graphviz.org/Documentation.php)

Lindbom L, Ribbing J & Jonsson EN (2004). Perl-speaks-NONMEM (PsN) - A Perl module for NONMEM related programming. Computer Methods and Programs in Biomedicine, 75(2), 85-94. doi:10.1016/j.cmpb.2003.11.003

Lindbom L, Pihlgren P & Jonsson N (2005). PsN-Toolkit - A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Computer Methods and Programs in Biomedicine, 79(3), 241-257. doi:10.1016/j.cmpb.2005.04.005

Other NONMEM reading: read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
scm <- plot_scm("E:/DrugX/ModelDevelopment/scm310")

## End(Not run)

Read NONMEM 7.2+ output into a list of lists.

Description

Read NONMEM 7.2+ output into a list of lists.

Usage

read_nm(fileName, directory = NULL, quiet = FALSE, ...)

Arguments

Value

A list of lists corresponding to a NONMEM output object.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_scm()

Examples

## Not run: 
nmOutput <- read_nm("run315.xml")

## End(Not run)

Read all NONMEM files for a single NONMEM run.

Description

Read all NONMEM files for a single NONMEM run.

Usage

read_nm_all(runNo, run_prefix = "run", directory = NULL, quiet = FALSE, ...)

Arguments

Details

The filename for loading is constructed as paste(run_prefix, runNo). To load a nonstandard file, simply set one of those values to NULL.

See Also

Other NONMEM reading: plot_scm(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Read (single or) multiple NONMEM tables from a single file

Description

Read (single or) multiple NONMEM tables from a single file

Usage

read_nm_multi_table(
  fileName,
  header = TRUE,
  ...,
  simplify = TRUE,
  table_start_pattern = "^TABLE NO"
)

Arguments

Value

A list of data.frames, or if only one is present and simplify=TRUE, a data.frame.

Author(s)

Bill Denney

See Also

Other NONMEM reading: plot_scm(), read_nm_all(), read_nmcov(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
read_nm_multi_table("run1.cov", row.names=1)

## End(Not run)

Read a standard NONMEM extension file

Description

Read a standard NONMEM extension file

Usage

read_nm_std_ext(fileName, extension, directory = NULL, ...)

Arguments

Value

NULL if the file does not exist or the value of read_nm_multi_table() if it does exist.

Examples

## Not run: 
read_nm_std_ext("run1", "phi")

## End(Not run)

Read in the NONMEM variance-covariance matrix.

Description

Read in the NONMEM variance-covariance matrix.

Usage

read_nmcov(fileName, quiet = FALSE, directory = NULL, ...)

Arguments

Value

A symmetrical variance-covariance matrix covering all model parameters.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmext(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
nmVcov <- read_nmcov("run315")

## End(Not run)

Read NONMEM output into a list.

Description

read_nmext returns a summary of a given NONMEM run, including termination messages, parameter estimates, and precision estimates. Minimally, the NONMEM output and '.ext' files must be available.

Usage

read_nmext(
  fileName,
  fileExt = ".lst",
  directory = NULL,
  quiet = FALSE,
  estNo = NULL,
  ...
)

Arguments

Value

A list of lists, containing 'Termination' (summary of NONMEM's termination output, including shrinkages and ETABAR estimates), 'OFV' (the objective function value), 'Thetas' (a vector of structural parameter estimates, or THETAs), 'Omega', a list of lists containing the OMEGA matrix, 'Sigma', a list of lists containing the SIGMA matrix, 'seThetas', a vector of standard errors for THETAs, 'seOmega', a list of lists containing standard errors for the OMEGA matrix, and 'seSigma', a list of lists containing standard errors for the SIGMA matrix.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmtables(), read_nm(), read_scm()

Examples

## Not run: 
read_nmext("run315")
read_nmext("run315", ".nmlst")

## End(Not run)

Reads NONMEM output tables.

Description

Reads NONMEM output tables.

Usage

read_nmtables(
  tableFiles = NULL,
  runNo = NULL,
  tabSuffix = "",
  tableNames = c("sdtab", "mutab", "patab", "catab", "cotab", "mytab", "extra", "xptab"),
  quiet = FALSE,
  directory = NULL,
  output_type = c("data.frame", "list"),
  ...
)

Arguments

Value

A data.frame or list of data.frames depending on the output_type argument.

Note

Adapted from Xpose 4 (https://CRAN.R-project.org/package=xpose4).

Author(s)

Bill Denney, Justin Wilkins, Niclas Jonsson, Andrew Hooker

References

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Jonsson EN, Karlsson MO. Xpose–an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed. 1999 Jan;58(1):51-64

See Also

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nm(), read_scm()

Examples

## Not run: 
tables <- read_nmtables(runNo=315)

## End(Not run)

Read PsN SCM output into a format suitable for further use.

Description

read_scm returns a summary of a Perl-speaks-NONMEM (PsN, https://uupharmacometrics.github.io/PsN/) SCM (stepwise covariate modeling) procedure. It depends on the presence of scmlog.txt and short_scmlog.txt files in the specified directory.

Usage

read_scm(dir, startPhase = "forward")

Arguments

Value

A list of data frames, containing

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Lindbom L, Ribbing J & Jonsson EN (2004). Perl-speaks-NONMEM (PsN) - A Perl module for NONMEM related programming. Computer Methods and Programs in Biomedicine, 75(2), 85-94. doi:10.1016/j.cmpb.2003.11.003

Lindbom L, Pihlgren P & Jonsson N (2005). PsN-Toolkit - A collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Computer Methods and Programs in Biomedicine, 79(3), 241-257. doi:10.1016/j.cmpb.2005.04.005

Other NONMEM reading: plot_scm(), read_nm_all(), read_nm_multi_table(), read_nmcov(), read_nmext(), read_nmtables(), read_nm()

Examples

## Not run: 
scm <- read_scm("E:/DrugX/ModelDevelopment/scm310")

## End(Not run)

Read NONMEM 7.2+ output into an R object.

Description

Read NONMEM 7.2+ output into an R object.

Usage

rnm(
  index,
  prefix = "run",
  pathNM,
  ndig = 3,
  ndigB = 3,
  ndigP = 1,
  Pci = 95,
  ext = ".lst",
  extmod = ".mod",
  Pvalues = TRUE,
  RawCI = FALSE,
  ...
)

Arguments

Details

The output list is composed of the following objects:

• "Theta"A data frame describing the structural (fixed-effect) parameters, containing parameter name, estimated value, standard error (SE), coefficient of variation (CV), lower and upper confidence limits (CIL and CIU, based on Pci), and P-value, calculated as 2 * (1 - pnorm(abs(theta/theta.se))).

• "Eta"A data frame describing the interindividual random-effects parameters, containing estimated value, standard error (SE), coefficient of variation (CV, calculated as abs(100*(SE/OMEGA))), coefficient of variation (EtaCV, calculated as 100*sqrt(OMEGA)), and shrinkage.

• "Epsilon"A data frame describing the residual random-effects parameters, containing estimated value, standard error (SE), coefficient of variation (CV, calculated as abs(100*(SE/OMEGA))), coefficient of variation (EtaCV, calculated as 100*sqrt(SIGMA)), and shrinkage.

• "CorTheta"A data frame containing the correlation matrix for fixed effects ("THETA").

• "CorOmega"A data frame containing the correlation matrix for interindividual random effects ("OMEGA").

• "CorSigma"A data frame containing the correlation matrix for residual random effects ("OMEGA").

• "OmegaMatrix"A data frame containing the "OMEGA" matrix.

• "SigmaMatrix"A data frame containing the "OMEGA" matrix.

• "CovMatrixTheta"A data frame containing the variance-covariance matrix for structural parameters (THETA).

• "CovMatrix"A data frame containing the complete variance-covariance matrix.

• "OFV"The objective function value.

• "ThetaString"A data frame containing all relevant fixed-effects parameter information, suitable for use in a table of parameter estimates. Contains parameter name, estimate, standard error, coefficient of variation, combined estimate and asymptotic confidence interval, and P-value.

• "EtaString"A data frame containing all relevant interindivudal random-effects parameter information, suitable for use in a table of parameter estimates. Contains parameter name, estimate (variance), standard error, coefficient of variation, percentage value (calculated as 100*sqrt(OMEGA)), and shrinkage.

• "EpsString"A data frame containing all relevant residual random-effects parameter information, suitable for use in a table of parameter estimates. Contains parameter name, estimate (variance), standard error, coefficient of variation, percentage value (calculated as 100*sqrt(SIGMA)), and shrinkage.

• "RunTime"Run time.

• "ConditionN"Condition number.

Value

A list containing information extracted from the NONMEM output.

Author(s)

Rik Schoemaker, rik.schoemaker@occams.com

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
nmOutput <- rnm("run315.lst")

## End(Not run)

Sample from the multivariate normal distribution using the OMEGA variance-covariance matrix to generate new sets of simulated ETAs from NONMEM output.

Description

Sample from the multivariate normal distribution using the OMEGA variance-covariance matrix to generate new sets of simulated ETAs from NONMEM output.

Usage

sample_omega(nmRun, n, seed)

Arguments

Value

A data frame containing n samples from the multivariate normal distribution, using the estimated NONMEM OMEGA variance-covariance matrix. This provides n sets of ETA estimates suitable for simulation of new patients.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 omDist <- sample_omega("run315", 5000, seed=740727)

## End(Not run)

Sample from the multivariate normal distribution using the SIGMA variance-covariance matrix to generate new sets of simulated EPSILONs from NONMEM output.

Description

Sample from the multivariate normal distribution using the SIGMA variance-covariance matrix to generate new sets of simulated EPSILONs from NONMEM output.

Usage

sample_sigma(nmRun, n, seed)

Arguments

Value

A data frame containing n samples from the multivariate normal distribution, using the estimated NONMEM SIGMA variance-covariance matrix. This provides n sets of EPSILON estimates suitable for simulation of new datasets.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 sigDist <- sample_sigma("run315", 5000, seed=740727)

## End(Not run)

Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.

Description

Sample from the multivariate normal distribution to generate new sets of parameters from NONMEM output.

Usage

sample_uncert(nmRun, n, seed)

Arguments

Value

A data frame containing n samples from the multivariate normal distribution, using NONMEM typical parameter estimates the NONMEM variance-covariance matrix (from the *.cov file). This provides n sets of parameter estimates sampled from the uncertainty distribution, suitable for simulation under model uncertainty.

Author(s)

Justin Wilkins, justin.wilkins@occams.com

See Also

NONMEM (https://www.iconplc.com/innovation/nonmem/)

Examples

## Not run: 
 nmMatrix <- sample_uncert("run315.xml", 5000, seed=740727)

## End(Not run)

Read NONMEM output into a list.

Description

table_rtf generates an RTF table from a data frame.

Usage

table_rtf(
  df,
  outFile = NULL,
  rtfFile = TRUE,
  boldHeader = TRUE,
  rowNames = FALSE,
  ...
)

Arguments

Value

An RTF table based on the data frame provided.

Author(s)

John Johnson, johndjohnson@gmail.com

References

https://www.r-bloggers.com/2008/10/another-solution-to-the-r-to-word-table-problem/

Examples

## Not run: 
scm <- read_scm("E:/DrugX/ModelDevelopment/scm310")
myRTF <- table_rtf(scm$forwardSummary)

## End(Not run)