---
title: LBT02
subtitle: Laboratory Test Results by Visit
---
------------------------------------------------------------------------
{{< include ../../_utils/envir_hook.qmd >}}
```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
# Preparation of an illustrative dataset
library(tern)
adsl <- random.cdisc.data::cadsl
adlb <- random.cdisc.data::cadlb
adlb_labels <- var_labels(adlb)
# For illustration purposes, the example focuses on ALT
# Measurements starting from baseline and excluding all screening visits
adlb <- subset(adlb, AVISIT != "SCREENING" & PARAMCD == "ALT")
adlb$AVISIT <- droplevels(adlb$AVISIT)
var_labels(adlb) <- adlb_labels
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adlb <- df_explicit_na(adlb)
```
```{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
l <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
split_rows_by(
var = "AVISIT",
split_fun = split_fun,
label_pos = "topleft",
split_label = obj_label(adlb$AVISIT)
) %>%
analyze_vars(vars = "AVAL")
result <- build_table(l,
df = adlb,
alt_counts_df = adsl
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant1")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Data Setup
```{r setup}
#| code-fold: show
```
::::
{{< include ../../_utils/save_results.qmd >}}
## `teal` App
::: {.panel-tabset .nav-justified}
## {{< fa regular file-lines fa-sm fa-fw >}} Preview
```{r teal, opts.label = c("skip_if_testing", "app")}
library(teal.modules.clinical)
## Data reproducible code
data <- teal_data()
data <- within(data, {
ADSL <- random.cdisc.data::cadsl
ADLB <- random.cdisc.data::cadlb
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
ADSL <- df_explicit_na(ADSL)
ADLB <- df_explicit_na(ADLB)
})
datanames <- c("ADSL", "ADLB")
names(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]
## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]
## Setup App
app <- init(
data = data,
modules = modules(
tm_t_summary_by(
label = "Laboratory Test Results by Visit",
dataname = "ADLB",
arm_var = choices_selected(
choices = variable_choices(ADSL, c("ARM", "ARMCD")),
selected = "ARM"
),
by_vars = choices_selected(
# note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`
choices = variable_choices(ADLB, c("PARAM", "AVISIT")),
selected = c("PARAM", "AVISIT")
),
summarize_vars = choices_selected(
choices = variable_choices(ADLB, c("AVAL")),
selected = c("AVAL")
),
useNA = "ifany",
paramcd = choices_selected(
choices = value_choices(ADLB, "PARAMCD", "PARAM"),
selected = "ALT"
)
)
),
filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL))
)
shinyApp(app$ui, app$server)
```
{{< include ../../_utils/shinylive.qmd >}}
:::
{{< include ../../repro.qmd >}}
