In saviclab/savictools: Streamlined Data Manipulation for Pharmacometrics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

library(tidyverse)

savictools

Overview

savictools is a collection of functions designed to make the pharmacometrics workflow faster, simpler, and more intuitive.

Data preparation

• cohort() generates simulation datasets for NONMEM.
• expwt() computes expected weight for children under 5.
• sparse() detects sparse vs. intensive PK sampling occasions.
• tad() computes time after dose.
• zscores() computes z-scores for WHO's anthropometric indicators.

Visualization

• cr_plot() visualizes the clinical relevance of covariates.
• etacorr_cat() and etacorr_cont() plot ETA-covariate correlations.
• pk_plot() creates PK concentration curves ("spaghetti plots").
• VPC() runs the Perl-speaks-Nonmem (PsN) vpc command from R, and is also a wrapper for xpose::VPC.

Workflow optimization

• param_table() creates nicely formatted tables of parameter estimates for single or multiple models.
• parse_all_sse() parses PsN sse-generated SSE_results.csv files and combines them into dataframes summarizing parameter statistics and error rates.

Installation

To install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("saviclab/savictools")

Usage

library(savictools)

Data preparation

cohort() is used to generate NONMEM-ready datasets for clinical PK trials, either by sampling from real datasets or generating synthetic data.

set.seed(12345)

pop_example

# 1. Sampling 20 individuals, above 10 kg and below 120 cm, with a fixed dose of
# 200 mg, observing every 4 hours for one day and dosing at times 0, 5, and 12.
# Note that the data has columns called "WT" and "HT".

inc <- "WT > 10 & HT < 120"
ot <- seq(0, 24, by = 4)
dt <- c(0, 5, 12)

cohort(
  pop_example,
  include = inc,
  n = 20,
  obs_times = ot,
  dose_times = dt,
  amt = 200
)


# 2. Simulating data. We assume WT and HT are normally distributed random
# variables, with means and standard deviations of 16 and 3.4 for WT and 132
# and 13.6 for HT.

p1 <-
  list("WT" = list("rnorm", 16, 3.4),
       "HT" = list("rnorm", 132, 13.6))

cohort(
  param = p1,
  include = inc,
  n = 20,
  pop_size = 1000,
  obs_times = ot,
  dose_times = dt,
  amt = 200,
  original_id = FALSE
)

# 3. As in (2), except we now define a dosing function.

dose_fun <- function(WT) {
  ifelse(WT < 16, 150,
         ifelse(WT < 20, 200, 250))
}

cohort(
  param = p1,
  include = inc,
  n = 20,
  pop_size = 500,
  obs_times = ot,
  dose_times = dt,
  original_id = FALSE,
  amt = dose_fun
)

set.seed(NULL)

expwt() calculates expected weight based on age and sex in under-5 children, adding a column called EXPWT.

expwt(pop_example)

sparse() automatically detects sparse vs. intensive PK sampling in a dataset.

library(ggplot2)

pk_example

sparse(pk_example, plot = TRUE)

sparse(pk_example, plot = TRUE) + 
  facet_wrap("ID", scales = "free_x", )

tad() computes time after dose.

# Deleting and re-calculating TAD
tad(pk_example)

zscores() computes z-scores indicating nutritional status.

zscores(pop_example, units = "months")

Visualization

saviclab/savictools documentation built on Dec. 7, 2023, 11:56 p.m.