LTG01

Lattice Plot of Laboratory Tests by Treatment Group Over Time

Lattice plots are natively handled by R, the examples below rely mostly on the package ggplot2.

Basic Plot

Code
# General mapping and "lattice" ("facet" in ggplot2 nomenclature).
g1 <- {
  ggplot(
    data = adlb,
    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)
  ) +
    facet_grid(LBTESTCD ~ ARM, scales = "free_y") +
    scale_shape_manual(values = pch)
}

# Add points and lines.
g1 <- g1 + geom_point()
g1 <- g1 + geom_line()
plot <- g1
plot

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Modifying Facets

The units describing rows of panes and the number of patients under each arm is specified by modifying facet_grid():

Code
# Include the units and the sample size N.
g2 <- g1 + facet_grid(
  paste0(LBTESTCD, "\n(", AVALU, ")") ~ ARM_N,
  scales = "free_y"
)

plot <- g2
plot

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Modifying X-Axis

The graphic elements are modified through usual ggplot2 functions. For instance, the x-axis could be improved as follows:

Code
g3 <- g2 + theme(
  axis.text.x = element_text(angle = 45, hjust = 1),
  axis.title = element_blank()
) + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)

plot <- g3
plot

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The functions stat_mean_ci and stat_median_ci from the tern package allow the addition of mean and/or median confidence intervals. The example below suggests a larger dataset, where the individual subject legend may not be relevant but the mean or the median are of special interest.

Pre-Processing

Code
# Datasets
adsl <- random.cdisc.data::cadsl %>% slice(1:40)
adlb <- random.cdisc.data::cadlb %>% filter(USUBJID %in% adsl$USUBJID)

# Pre-processing
adlb$AVISIT_txt <- adlb$AVISIT
adlb$AVISIT <- as.numeric(adlb$AVISIT)
adlb$ARM_N <- adlb$ARM
levels(adlb$ARM_N) <- with(
  data = adlb,
  paste0(
    levels(ARM_N), " (N = ",
    tapply(SUBJID, ARM_N, function(x) length(unique(x))), ")"
  )
)

# Plot utils
npch <- 1:25
npatients <- length(unique(adlb$SUBJID))
pch <- c(
  rep(npch, times = npatients %/% length(npch)),
  npch[1:(npatients %% length(npch))]
)

Basic Plot

Code
# General mapping and "lattice" ("facet" in ggplot2 nomenclature)
g4 <- {
  ggplot(
    data = adlb,
    mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)
  ) +
    facet_grid(LBTESTCD ~ ARM_N, scales = "free_y") +
    scale_shape_manual(values = pch) +
    scale_color_manual(values = rep(getOption("ggplot2.discrete.colour"), 2))
}

# Add points and lines.
# Note that with so many patients, legend might not be useful and transparency
#   is advisable.
g4 <- g4 + geom_point(alpha = .3)
g4 <- g4 + geom_line(alpha = .3)
g4 <- g4 + guides(colour = "none", shape = "none")
g4 <- g4 + theme(
  axis.text.x = element_text(angle = 45, hjust = 1),
  axis.title  = element_blank()
)
g4 <- g4 + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)
plot <- g4
plot

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Adding Mean

Code
# Add the mean along with the 95% CI at every visit.
g51 <- g4 + stat_summary(
  fun = mean, linewidth = 1, geom = "line",
  aes(group = 1, linetype = "Mean +/- 95% CI")
)
g51 <- g51 + stat_summary(
  fun.data = tern::stat_mean_ci, geom = "errorbar",
  aes(group = 1, linetype = "Mean +/- 95% CI")
)
plot <- g51 + guides(linetype = guide_legend(title = NULL))
plot

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Adding Median

Code
# Instead of a mean, the median could be more relevant.
g52 <- g51 + stat_summary(
  fun = median, linewidth = 1, geom = "line",
  aes(group = 1, linetype = "Median +/- 95% CI")
)
g52 <- g52 + stat_summary(
  fun.data = tern::stat_median_ci, geom = "errorbar",
  aes(group = 1, linetype = "Median +/- 95% CI")
)
plot <- g52 + guides(linetype = guide_legend(title = "Aggregate"))
plot

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Changing Confidence Level

Code
# Change the confidence level of interval for the median.
# Note: check `?stat_mean_ci()` and `?stat_median_ci()` for further fine tuning.
g53 <- g4 + stat_summary(
  fun = median, linewidth = 1, geom = "line",
  aes(group = 1, linetype = "Median +/- 80% CI")
)
g53 <- g53 + stat_summary(
  fun.data = function(x) tern::stat_median_ci(x, conf_level = 0.8),
  geom = "errorbar", aes(group = 1, linetype = "Median +/- 80% CI")
)
plot <- g53 + guides(linetype = guide_legend(title = NULL))
plot

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Code
library(tern)
library(teal.modules.clinical)
library(ggplot2)
library(dplyr)
library(nestcolor)

# Datasets
adsl <- random.cdisc.data::cadsl %>% slice(1:8)
adlb <- random.cdisc.data::cadlb %>% filter(USUBJID %in% adsl$USUBJID)

# Pre-processing
adlb$AVISIT_txt <- adlb$AVISIT
adlb$AVISIT <- as.numeric(adlb$AVISIT)
adlb$ARM_N <- adlb$ARM
levels(adlb$ARM_N) <- with(
  data = adlb,
  paste0(
    levels(ARM_N), " (N = ",
    tapply(SUBJID, ARM_N, function(x) length(unique(x))), ")"
  )
)

# Plot utils
npch <- 1:25
npatients <- length(unique(adlb$SUBJID))
pch <- c(
  rep(npch, times = npatients %/% length(npch)),
  npch[1:(npatients %% length(npch))]
)

Reproducibility

Timestamp

[1] "2024-11-02 17:40:35 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
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 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2.9079 2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.git@53c423d)
 teal.code             * 0.5.0.9012  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.code.git@86aa265)
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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