---
title: LBT03
subtitle: Laboratory Test Results Change from Baseline by Visit
---
------------------------------------------------------------------------
{{< include ../../_utils/envir_hook.qmd >}}
```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(dplyr)
library(tern)
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)
saved_labels <- var_labels(adlb)
adlb_f <- adlb %>%
filter(
PARAM == "C-Reactive Protein Measurement",
!(ARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8"),
AVISIT != "SCREENING"
) %>%
dplyr::mutate(
AVISIT = droplevels(AVISIT),
ABLFLL = ABLFL == "Y"
)
var_labels(adlb_f) <- c(saved_labels, "")
```
```{r include = FALSE}
webr_code_labels <- c("setup")
```
{{< include ../../_utils/webr_no_include.qmd >}}
## Output
::::: panel-tabset
## Standard Table
The `LBT03` template is the result of a junction between the analysis of `AVAL` at baseline and `CHG` at visit time. `AVAL` is summarized for baseline visits and and `CHG` is summarized for post-baseline visits.
::: {.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("ARM") %>%
split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adlb_f$AVISIT)) %>%
summarize_change(
"CHG",
variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
na.rm = TRUE
)
result <- build_table(
lyt = lyt,
df = adlb_f,
alt_counts_df = adsl
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant1")
```
{{< include ../../_utils/webr.qmd >}}
:::
In the final step, a new variable is derived from `AVISIT` that can specify the method of estimation of the evaluated change.
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant2, test = list(result_v2 = "result")}
adlb_f <- adlb_f %>% mutate(AVISIT_header = recode(AVISIT,
"BASELINE" = "BASELINE",
"WEEK 1 DAY 8" = "WEEK 1 DAY 8 value minus baseline",
"WEEK 2 DAY 15" = "WEEK 2 DAY 15 value minus baseline",
"WEEK 3 DAY 22" = "WEEK 3 DAY 22 value minus baseline",
"WEEK 4 DAY 29" = "WEEK 4 DAY 29 value minus baseline",
"WEEK 5 DAY 36" = "WEEK 5 DAY 36 value minus baseline"
))
# Define the split function
split_fun <- drop_split_levels
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT_header",
split_fun = split_fun,
label_pos = "topleft",
split_label = obj_label(adlb_f$AVISIT)
) %>%
summarize_change(
"CHG",
variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
na.rm = TRUE
)
result <- build_table(
lyt = lyt,
df = adlb_f,
alt_counts_df = adsl
)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant2")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Data Setup
For illustration purposes, this example focuses on "C-Reactive Protein Measurement" starting from baseline, while excluding visit at week 1 for subjects who were randomized to the placebo group.
```{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, we pre-process and manually define the variable "Baseline or Absolute Change from Baseline".
```{r teal, opts.label = c("skip_if_testing", "app")}
library(teal.modules.clinical)
## Data reproducible code
data <- teal_data()
data <- within(data, {
ADSL <- df_explicit_na(random.cdisc.data::cadsl)
ADLB <- df_explicit_na(random.cdisc.data::cadlb) %>%
filter(
!(ARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8"),
AVISIT != "SCREENING"
) %>%
mutate(
AVISIT = droplevels(AVISIT),
ABLFLL = ABLFL == "Y",
AVISIT_header = recode(AVISIT,
"BASELINE" = "BASELINE",
"WEEK 1 DAY 8" = "WEEK 1 DAY 8 value minus baseline",
"WEEK 2 DAY 15" = "WEEK 2 DAY 15 value minus baseline",
"WEEK 3 DAY 22" = "WEEK 3 DAY 22 value minus baseline",
"WEEK 4 DAY 29" = "WEEK 4 DAY 29 value minus baseline",
"WEEK 5 DAY 36" = "WEEK 5 DAY 36 value minus baseline"
)
) %>%
group_by(USUBJID, PARAM) %>%
mutate(
AVAL_CHG = AVAL - (!ABLFLL) * sum(AVAL * ABLFLL)
) %>%
ungroup() %>%
col_relabel(
AVAL_CHG = "Baseline or Absolute Change from Baseline",
ABLFLL = "Baseline Flag (TRUE/FALSE)",
AVISIT_header = "Analysis Visit"
)
})
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 Change from Baseline 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_header")),
selected = c("PARAM", "AVISIT_header")
),
summarize_vars = choices_selected(
choices = variable_choices(ADLB, c("AVAL", "CHG", "AVAL_CHG")),
selected = c("AVAL_CHG")
),
useNA = "ifany",
paramcd = choices_selected(
choices = value_choices(ADLB, "PARAMCD", "PARAM"),
selected = "CRP"
)
)
),
filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL))
)
shinyApp(app$ui, app$server)
```
{{< include ../../_utils/shinylive.qmd >}}
:::
{{< include ../../repro.qmd >}}
