Code
mmrm() registered as emmeans extensionMMRMG02
Forest Plot for Mixed-Effect Model Repeated Measures
For the following part, an MMRM model is fitted for the dataset and from this result forest plots are constructed for each set of subgroups, treatment arm, and visit that is of interest.
First an MMRM model is fitted for the whole dataset.
Applying the tern.mmrm::extract_mmrm_subgroups function prepares the obtained mmrm_results for a specific visit - in this case we use the SCREENING visit - and treatment arm relative to the reference arm. From these results a table is obtained using the tern.mmrm::tabulate_mmrm_subgroups function from which the graphic can be directly obtained with tern::g_forest.
Here we compare ARM A with the reference arm, ARM B.
Code
df_a <- extract_mmrm_subgroups(
fit = mmrm_results,
visit = "SCREENING",
subgroups = c("group", "SEX"),
treatment_arm = "ARM A"
)
tab_a <- basic_table() %>%
tabulate_mmrm_subgroups(
df = df_a,
vars = c("n_tot", "diff", "ci", "pval")
)
plot <- g_forest(
tab_a,
logx = FALSE,
xlim = c(-5, 2.5),
x_at = c(-5, -2.5, 0, 2.5),
vline = 0
)
plot
Then we compare ARM C with ARM B.
Code
df_c <- extract_mmrm_subgroups(
fit = mmrm_results,
visit = "SCREENING",
subgroups = c("group", "SEX"),
treatment_arm = "ARM C"
)
tab_c <- basic_table() %>%
tabulate_mmrm_subgroups(
df = df_c,
vars = c("n_tot", "diff", "ci", "pval")
)
plot <- g_forest(
tab_c,
logx = FALSE,
xlim = c(-5, 2.5),
x_at = c(-5, -2.5, 0, 2.5),
vline = 0
)
plot
Code
library(dplyr)
library(tern.mmrm)
library(nestcolor)
adsl <- random.cdisc.data::cadsl
adqs <- random.cdisc.data::cadqs
adqs_f <- adqs %>%
dplyr::filter(PARAMCD == "FKSI-FWB" & !AVISIT %in% c("BASELINE")) %>%
droplevels() %>%
dplyr::mutate(ARMCD = factor(ARMCD, levels = c("ARM B", "ARM A", "ARM C"))) %>%
dplyr::mutate(
AVISITN = rank(AVISITN) %>%
as.factor() %>%
as.numeric() %>%
as.factor()
)
# Simulation of groups.
set.seed(2)
adqs_f_with_groups <- rbind(
within(
adqs_f[sample(seq_len(nrow(adqs_f)), size = 1 / 2 * nrow(adqs_f)), ],
group <- "subpopulation 1"
),
within(
adqs_f,
{
group <- "subpopulation 2"
AVAL <- AVAL + rnorm(length(AVAL), mean = 10, sd = 2)
USUBJID <- paste0(USUBJID, "-S2")
}
)
)
adqs_f_with_groups$group <- factor(adqs_f_with_groups$group)Reproducibility
Timestamp
[1] "2024-11-02 17:37:15 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
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tz Etc/UTC
date 2024-11-02
pandoc 3.4 @ /usr/bin/ (via rmarkdown)
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