pmxhelpr

The goal of pmxhelpr is to make pharmacometrics workflows more standardized, efficient, and reproducible. This package provides helper and wrapper functions for common steps in the pharmacometrics analysis workflow, such as exploratory data analysis, model development, model evaluation, and model application.

Installation

You can install the development version of pmxhelpr from GitHub with:

# install.packages("devtools")
devtools::install_github("ryancrass/pmxhelpr")

Function Naming Conventions

Functions in this package use the following naming conventions:

• Wrapper functions: ReturnObject_WrappedFunction_Purpose
  • model_mread_load() wraps mrgsolve::mread_cache() to read internal package model files returning an mrgmod object.
  • df_mrgsim_addpred() wraps mrgsolve::mrgsim() including mrgsolve::zero_re() to add population predictions (PRED) to the input data returning a data.frame.
  • df_mrgsim_replicate() wraps mrgsolve::mrgsim() to replicate the input data returning a data.frame.
  • plot_vpc_exactbins() wraps vpc::vpc() to generate a VPC plot using exact time bins returning a ggplot object.
• Helper functions: ReturnObject_Purpose
  • plot_dvtime returns a ggplot object with a dependent variable plotted versus time
  • plot_doseprop returns a ggplot object with log-log regression of exposure metrics versus dose
  • df_doseprop returns a data.frame containing parameters from a log-log regression of exposure metrics versus dose
  • plot_popgof returns a ggplot object with observed, population-predicted, and individual-predicted values plotted versus time
  • df_nobsbin returns a summary data.frame with counts of the number of missing and non-missing observations per bin.
  • df_pcdv returns a data.frame containing the prediction-corrected dependent variable.
  • plot_vpclegend returns a ggplot object containing a legend for a VPC plot generated using plot_vpc_exactbins

Example Exploratory Data Analysis Workflow

This is a basic example which illustrates a simple exploratory data analysis workflow using pmxhelpr:

library(pmxhelpr)
library(dplyr)
library(ggplot2)
library(mrgsolve)
library(vpc)
library(patchwork)
library(withr)

#Read internal analysis-ready dataset for an example Phase 1 study
glimpse(data_sad)

#Plot data
data <- data_sad %>% 
  mutate(Dose = paste(DOSE, "mg"), 
         Dose_f = factor(Dose, levels = c("10 mg", "50 mg", "100 mg", "200 mg", "400 mg")))
         
plot_dvtime(data = data, dv_var = c(DV = "ODV"), cent = "median", col_var = "Dose_f",
            ylab = "Concentration (ng/mL)", timeu = "hours")

#Assess dose proportionality in the fasted state
glimpse(data_sad_nca)
data_sad_nca_part1 <- filter(data_sad_nca, PART == "Part 1-SAD")

#Tabulated dose-proportionality
table <- df_doseprop(data_sad_nca, metrics = c("aucinf.obs", "cmax"))
table

#Visualize dose-proportionality
plot_doseprop(data_sad_nca_part1, metrics = c("aucinf.obs", "cmax"))

Example Visual Exploratory Data Analysis Workflow

This is a basic example which illustrates a simple VPC workflow using pmxhelpr:

library(pmxhelpr)
library(dplyr)
library(ggplot2)
library(mrgsolve)
library(vpc)
library(patchwork)
library(withr)

#Read internal mrgsolve model file
model <- model_mread_load("model")

#Simulated replicates of the dataset using mrgsim 
simout <- df_mrgsim_replicate(data = data, model = model,replicates = 100,
                              dv_var = "ODV",
                              num_vars = c("CMT", "LLOQ", "EVID", "MDV", "WTBL", "FOOD"),
                              char_vars = c("USUBJID", "PART", "Dose_f"))
glimpse(simout)

#Plot output in a Prediction-corrected Visual Predictive Check (VPC)
  #Exact nominal time bins present in data_sad ("NTIME") are used to plot summary statistics
  #Actual time ("TIME") is used to plot observed data points, which are also prediction-corrected if pcvpc=TRUE


plot_obj_food <- plot_vpc_exactbins(
  sim = mutate(simout, FOOD_f = factor(FOOD, levels = c(0,1), labels = c("Fasted", "Fed"))), 
  strat_var = "FOOD_f",
  pcvpc = TRUE
) + 
  scale_y_log10(guide = "axis_logticks")

plot_obj_food

#Add Legend
plot_obj_leg <- plot_vpclegend()
plot_obj_leg

plot_obj_food_wleg <- plot_obj_food + plot_obj_leg + plot_layout(heights = c(2,1))
plot_obj_food_wleg