pmxpartab

This R package produces nice looking parameter tables for pharmacometric modeling results with ease. It is completely agnostic to the modeling software that was used.

Installation

This package is not yet on CRAN. To install the latest development version directly from GitHub:

require(remotes)
remotes::install_github("benjaminrich/pmxpartab")

Usage

The creation of the parameter table proceeds in 2 steps:

Generate an intermediate data.frame from model outputs and metadata.
Generate a formatted HTML table from that intermediate data.frame.

Both the model outputs and metadata are provided as R lists (it is a separate problem to extract the outputs from the modeling software into the required list format).

Here is an example (in this example, YAML is used to give a clear and concise representation of outputs and metadata, but this is not required).

library(yaml)

outputs <- yaml.load("
est:
  CL:    0.482334
  VC:    0.0592686
  CL_WT: 0.750000
  VC_WT: 1.00000
  nCL:   0.315414
  nVC:   0.536025
  ERRP:  0.0508497
se:
  CL:    0.0138646
  VC:    0.0055512
  nCL:   0.0188891
  nVC:   0.0900352
  ERRP:  0.0018285
fixed:
  CL:    no
  VC:    no
  CL_WT: yes
  VC_WT: yes
  nCL:   no
  nVC:   no
  ERRP:  no
shrinkage:
  nCL:  9.54556
  nVC:  47.8771
")

meta <- yaml.load("
parameters:
- name:  CL
  label: 'Clearance'
  units: 'L/h'
  type:  Structural

- name:  VC
  label: 'Volume'
  units: 'L'
  type:  Structural
  trans: 'exp'
  
- name:  CL_WT
  label: 'Weight on Clearance'
  type:  CovariateEffect

- name:  VC_WT
  label: 'Weight on Volume'
  type:  CovariateEffect
  
- name:  nCL
  label: 'On Clearance'
  type:  IIV
  trans: 'SD (CV%)'
  
- name:  nVC
  label: 'On Volume'
  type:  IIV
  trans: 'SD (CV%)'
  
- name:  ERRP
  label: 'Proportional Error'
  units: '%'
  type:  RUV
  trans: '%'
")

parframe <- pmxparframe(outputs, meta)
parframe
#>    name               label units            type    trans fixed      est
#> 1    CL           Clearance   L/h      Structural     <NA> FALSE 0.482334
#> 2    VC              Volume     L      Structural      exp FALSE 1.061060
#> 3 CL_WT Weight on Clearance  <NA> CovariateEffect     <NA>  TRUE 0.750000
#> 4 VC_WT    Weight on Volume  <NA> CovariateEffect     <NA>  TRUE 1.000000
#> 5   nCL        On Clearance  <NA>             IIV SD (CV%) FALSE 0.315414
#> 6   nVC           On Volume  <NA>             IIV SD (CV%) FALSE 0.536025
#> 7  ERRP  Proportional Error     %             RUV        % FALSE 5.084970
#>            se       rse     lci95     uci95         pval shrinkage
#> 1 0.013864600  2.874481 0.4551594 0.5095086 0.000000e+00        NA
#> 2 0.005890157  0.555120 1.0495781 1.0726679 0.000000e+00        NA
#> 3          NA        NA        NA        NA           NA        NA
#> 4          NA        NA        NA        NA           NA        NA
#> 5 0.018889100  5.988669 0.2783914 0.3524366 0.000000e+00   9.54556
#> 6 0.090035200 16.796829 0.3595560 0.7124940 2.624601e-09  47.87710
#> 7 0.182850000  3.595891 4.7265840 5.4433560 0.000000e+00        NA

pmxpartab(parframe)

Which produces:

For more information, read the vignette.