vs_nonmem {mapbayr}

Compare results to NONMEM .phi

Description

Compare results to NONMEM .phi

Usage

read_nmphi(x)

merge_phi(mapbayr_phi, nonmem_phi)

plot_phi(merged_phi, only_ETA = TRUE)

summarise_phi(
  merged_phi,
  group,
  only_ETA = TRUE,
  levels = c(Excellent = 0, Acceptable = 0.001, Discordant = 0.1)
)

bar_phi(summarised_phi, xaxis = NULL, facet = NULL)

Arguments

x

full path to a .phi file generated by NONMEM

mapbayr_phi

results of mapbayr estimations, in the form of a tibble data.frame, typically obtained from get_phi()

nonmem_phi

results of NONMEM estimations, in the form of a tibble data.frame, typically obtained from read_nmphi()

merged_phi

merged results of estimations, typically obtained from merge_phi()

only_ETA

filter the data with type=="ETA" (a logical, default is TRUE)

group

one or multiple variables to group_by()

levels

a named vector of length 3 in order to classify the absolute differences. Default cut-offs are 0.1% and 10% in the parameters space.

summarised_phi

summarized results of estimations, typically obtained from summarise_phi()

xaxis

optional. A character value, that correspond to a variable in data, passed to the x-axis to plot multiple bars side-by-side.

facet

a formula, that will be passed to ggplot2::facet_wrap()

Details

These functions were made to easily compare the results of mapbayr to NONMEM. For instance, it could be useful in the case of the transposition of a pre-existing NONMEM model into mapbayr. For this, you need to code your model in both mapbayr and NONMEM, and perform the MAP-Bayesian estimation on the same dataset. Ideally, the latter contains a substantial number of patients. NONMEM returns the estimations results into a .phi file.

Use read_nmphi() to parse the NONMEM .phi file into a convenient tibble data.frame with the columns:

• SUBJECT_NO, ID: Subject identification.

• ETA1, ETA2, ..., ETAn: Point estimates of eta.

• ETC1_1, ETC2_1, ETC2_2, ..., ETCn_n: Variance-covariance matrix of estimation.

• OBJ: objective function value

Use get_phi() to access to the estimations of mapbayr with the same "phi" format.

Use merge_phi() to combine mapbayr and NONMEM "phi files" into a single long-form data.frame with the columns:

• SUBJECT_NO, ID: Subject identification.

• variable name and its type: ETA (point estimate), VARIANCE (on-diagonal element of the matrix), COVARIANCE (off-diagonal), and OBJ.

• mapbayr and nonmem: corresponding values

• adiff: absolute difference between mapbayr and nonmem values.

Use plot_phi() to graphically represent adiff vs variable. Alternatively, the table returned by merge_phi() is easy to play with in order to derive performance statistics or the graphical plot of your choice.

Use summarise_phi() to classify the estimation as "Excellent", "Acceptable" or "Discordant", over the whole dataset or by group.

Use bar_phi() to graphically represent the proportion of the aforementioned classification as bar plot.

Value

• read_nmphi: a tibble data.frame with a format close to the original .phi file

• merge_phi: a long-form tibble data.frame with results of mapbayr and NONMEM

• summarise_phi: a summarized tibble data.frame classifying the performance of mapbayr and NONMEM

• plot_phi, bar_phi: a ggplot2 object

Examples

library(mapbayr)
nmphi <- read_nmphi(system.file("nm001", "run001.phi", package = "mapbayr"))
mapbayrphi <- get_phi(est001)

merged <- merge_phi(mapbayrphi, nmphi)
plot_phi(merged)

summarised <- summarise_phi(merged)
bar_phi(summarised)


# Analyse the results of multiple runs simultaneously

#Example dataset that represents 3 runs
merge3 <- dplyr::bind_rows(merged, merged, merged, .id = "RUN")
merge3$adiff <- 10 ^ runif(nrow(merge3), -8, 0)

summarised3 <- summarise_phi(merge3, group = RUN)
bar_phi(summarised3, xaxis = "RUN")