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.
# 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
The units describing rows of panes and the number of patients under each arm is specified by modifying facet_grid():
The graphic elements are modified through usual ggplot2 functions. For instance, the x-axis could be improved as follows:
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.
# 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$SUBJID))
pch <- c(
rep(npch, times = npatients %/% length(npch)),
npch[1:(npatients %% length(npch))]
)# General mapping and "lattice" ("facet" in ggplot2 nomenclature)
g4 <- {
ggplot(
data = adlb,
mapping = aes(x = AVISIT, y = AVAL, colour = SUBJID, shape = SUBJID)
) +
facet_grid(LBTESTCD ~ ARM_N, scales = "free_y") +
scale_shape_manual(values = pch) +
scale_color_manual(values = rep(getOption("ggplot2.discrete.colour"), 2))
}
# Add points and lines.
# Note that with so many patients, legend might not be useful and transparency
# is advisable.
g4 <- g4 + geom_point(alpha = .3)
g4 <- g4 + geom_line(alpha = .3)
g4 <- g4 + guides(colour = "none", shape = "none")
g4 <- g4 + theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_blank()
)
g4 <- g4 + scale_x_continuous(breaks = adlb$AVISIT, labels = adlb$AVISIT_txt)
plot <- g4
plot
# Add the mean along with the 95% CI at every visit.
g51 <- g4 + stat_summary(
fun = mean, linewidth = 1, geom = "line",
aes(group = 1, linetype = "Mean +/- 95% CI")
)
g51 <- g51 + stat_summary(
fun.data = tern::stat_mean_ci, geom = "errorbar",
aes(group = 1, linetype = "Mean +/- 95% CI")
)
plot <- g51 + guides(linetype = guide_legend(title = NULL))
plot
# Instead of a mean, the median could be more relevant.
g52 <- g51 + stat_summary(
fun = median, linewidth = 1, geom = "line",
aes(group = 1, linetype = "Median +/- 95% CI")
)
g52 <- g52 + stat_summary(
fun.data = tern::stat_median_ci, geom = "errorbar",
aes(group = 1, linetype = "Median +/- 95% CI")
)
plot <- g52 + guides(linetype = guide_legend(title = "Aggregate"))
plot
# Change the confidence level of interval for the median.
# Note: check `?stat_mean_ci()` and `?stat_median_ci()` for further fine tuning.
g53 <- g4 + stat_summary(
fun = median, linewidth = 1, geom = "line",
aes(group = 1, linetype = "Median +/- 80% CI")
)
g53 <- g53 + stat_summary(
fun.data = function(x) tern::stat_median_ci(x, conf_level = 0.8),
geom = "errorbar", aes(group = 1, linetype = "Median +/- 80% CI")
)
plot <- g53 + guides(linetype = guide_legend(title = NULL))
plot
library(tern)
library(teal.modules.clinical)
library(ggplot2)
library(dplyr)
library(nestcolor)
# Datasets
adsl <- random.cdisc.data::cadsl %>% slice(1:8)
adlb <- random.cdisc.data::cadlb %>% filter(USUBJID %in% adsl$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))]
)[1] "2026-06-17 18:46:29 UTC"─ Session info ───────────────────────────────────────────────────────────────
setting value
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