---
title: CMT01
subtitle: Concomitant Medications (GNEDrug Legacy Coding)
---
------------------------------------------------------------------------
{{< include ../../_utils/envir_hook.qmd >}}
```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(dplyr)
library(tern)
# The following tables require ADCM data structure that follows the legacy concomitant medication coding.
# WHO coding is not used.
adsl <- random.cdisc.data::cadsl
adcm <- random.cdisc.data::cadcm
# Below step is to set up example data based on legacy standard.
adcm <- adcm %>%
select(-starts_with("ATC")) %>%
unique()
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adcm <- df_explicit_na(adcm)
adcm_con <- adcm %>%
filter(ATIREL == "CONCOMITANT")
adcm_prior <- adcm %>%
filter(ATIREL == "PRIOR")
```
```{r include = FALSE}
webr_code_labels <- c("setup")
```
{{< include ../../_utils/webr_no_include.qmd >}}
## Output
::::::: panel-tabset
## Standard Table
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant1, test = list(result_v1 = "result")}
# Define the split function
split_fun <- drop_split_levels
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
analyze_num_patients(
vars = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one treatment",
nonunique = "Total number of treatments"
)
) %>%
split_rows_by("CMCLAS",
split_fun = split_fun,
child_labels = "visible",
nested = FALSE,
indent_mod = 1L,
label_pos = "topleft",
split_label = obj_label(adcm_con$CMCLAS)
) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one treatment",
nonunique = "Total number of treatments"
)
) %>%
count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
append_varlabels(adcm_con, "CMDECOD", indent = 1L)
result <- build_table(
lyt = lyt,
df = adcm_con,
alt_counts_df = adsl
) %>%
prune_table() %>%
sort_at_path(
path = c("CMCLAS", "*", "CMDECOD"),
scorefun = score_occurrences
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant1")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Table Showing Medications Prior <br/> to the Start of the Study
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant2, test = list(result_v2 = "result")}
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
analyze_num_patients(
vars = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one treatment",
nonunique = "Total number of treatments"
)
) %>%
split_rows_by("CMCLAS",
split_fun = split_fun,
child_labels = "visible",
nested = FALSE,
label_pos = "topleft",
split_label = obj_label(adcm_con$CMCLAS)
) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one treatment",
nonunique = "Total number of treatments"
)
) %>%
count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
append_varlabels(adcm_prior, "CMDECOD", indent = 1L)
result <- build_table(
lyt = lyt,
df = adcm_prior,
alt_counts_df = adsl
) %>%
prune_table() %>%
sort_at_path(
path = c("CMCLAS", "*", "CMDECOD"),
scorefun = score_occurrences
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant2")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Table with Subtotal Per <br/> Medication Class Suppressed
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant3, test = list(result_v3 = "result")}
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
analyze_num_patients(
vars = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one treatment",
nonunique = "Total number of treatments"
)
) %>%
split_rows_by("CMCLAS",
split_fun = split_fun, child_labels = "visible",
nested = FALSE,
label_pos = "topleft",
split_label = obj_label(adcm_con$CMCLAS)
) %>%
summarize_num_patients(
var = "USUBJID",
.labels = c(unique = "Total number of patients with at least one treatment"),
.stats = "unique"
) %>%
count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
append_varlabels(adcm_con, "CMDECOD", indent = 1L)
result <- build_table(
lyt = lyt,
df = adcm_con,
alt_counts_df = adsl
) %>%
prune_table() %>%
sort_at_path(
path = c("CMCLAS", "*", "CMDECOD"),
scorefun = score_occurrences
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant3")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Table Sorted by Total Column Showing <br/> Additional "All Patients" Column
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant4, test = list(result_v4 = "result")}
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
analyze_num_patients(
vars = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
"Total number of patients with at least one treatment (%)",
"Total number of treatments"
)
) %>%
split_rows_by(
"CMCLAS",
split_fun = split_fun,
child_labels = "visible",
nested = FALSE,
label_pos = "topleft",
split_label = obj_label(adcm_con$CMCLAS)
) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
"Total number of patients with at least one treatment (%)",
"Total number of treatments"
)
) %>%
count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
append_varlabels(adcm_con, "CMDECOD", indent = 1L)
result <- build_table(
lyt = lyt,
df = adcm_con,
alt_counts_df = adsl
) %>%
prune_table() %>%
sort_at_path(
path = c("CMCLAS", "*", "CMDECOD"),
scorefun = score_occurrences,
decreasing = TRUE
) %>%
sort_at_path(
path = c("CMCLAS"),
scorefun = cont_n_allcols,
decreasing = TRUE
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant4")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Data Setup
```{r setup}
#| code-fold: show
```
:::::::
{{< include ../../_utils/save_results.qmd >}}
## `teal` App
::: {.panel-tabset .nav-justified}
## {{< fa regular file-lines fa-sm fa-fw >}} Preview
Here, `adcm` is processed to keep only one path per treatment.
```{r teal, opts.label = c("skip_if_testing", "app")}
library(teal.modules.clinical)
## Data reproducible code
data <- teal_data()
data <- within(data, {
library(dplyr)
ADSL <- random.cdisc.data::cadsl
ADCM <- random.cdisc.data::cadcm
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
ADSL <- df_explicit_na(ADSL)
ADCM <- df_explicit_na(ADCM)
ADCM <- ADCM %>%
group_by(STUDYID, USUBJID, CMDECOD) %>%
slice(1) %>%
ungroup()
})
datanames <- c("ADSL", "ADCM")
names(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]
## Reusable Configuration For Modules
ADCM <- data[["ADCM"]]
## Setup App
app <- init(
data = data,
modules = modules(
tm_t_events(
label = "Concomitant Medication Table",
dataname = "ADCM",
arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
llt = choices_selected(
choices = variable_choices(ADCM, "CMDECOD"),
selected = "CMDECOD"
),
hlt = choices_selected(
choices = variable_choices(ADCM, "CMCLAS"),
selected = c("CMCLAS")
),
add_total = TRUE,
event_type = "treatment"
)
)
)
shinyApp(app$ui, app$server)
```
{{< include ../../_utils/shinylive.qmd >}}
:::
{{< include ../../repro.qmd >}}
