---
title: LGRT02
subtitle: Multi-Variable Logistic Regression
---
------------------------------------------------------------------------
{{< include ../../_utils/envir_hook.qmd >}}
```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(dplyr)
library(tern)
adsl <- random.cdisc.data::cadsl
adrs <- random.cdisc.data::cadrs
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adrs <- df_explicit_na(adrs)
adsl <- adsl %>%
dplyr::filter(SEX %in% c("F", "M"))
adrs <- adrs %>%
dplyr::filter(PARAMCD == "BESRSPI") %>%
dplyr::mutate(
Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
SEX = factor(SEX, c("M", "F")),
RACE = factor(
RACE,
levels = c(
"AMERICAN INDIAN OR ALASKA NATIVE", "ASIAN", "BLACK OR AFRICAN AMERICAN",
"WHITE", "MULTIPLE", "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER"
)
)
) %>%
var_relabel(Response = "Response", SEX = "Sex", RACE = "Race")
```
```{r include = FALSE}
webr_code_labels <- c("setup")
```
{{< include ../../_utils/webr_no_include.qmd >}}
## Output
::::::: panel-tabset
## Multi-Variable Logistic Regression
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant1, test = list(result_v1 = "result")}
model <- fit_logistic(
adrs,
variables = list(response = "Response", arm = "ARMCD", covariates = c("SEX", "AGE"))
)
conf_level <- 0.95
df <- broom::tidy(model, conf_level = conf_level)
# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")
result <- basic_table() %>%
summarize_logistic(
conf_level = conf_level,
drop_and_remove_str = "_NA_"
) %>%
append_topleft("Logistic regression") %>%
build_table(df = df)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant1")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Multi-Variable Logistic Regression <br/> with Interaction Term
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant2, test = list(result_v2 = "result")}
model <- fit_logistic(
adrs,
variables = list(
response = "Response",
arm = "ARMCD",
covariates = c("SEX", "AGE"),
interaction = "SEX"
)
)
conf_level <- 0.95
df <- broom::tidy(model, conf_level = conf_level)
# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")
result <- basic_table() %>%
summarize_logistic(
conf_level = conf_level,
drop_and_remove_str = "_NA_"
) %>%
append_topleft("Logistic regression with interaction") %>%
build_table(df = df)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant2")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Multi-Variable Logistic Regression <br/> Specifying Covariates
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant3, test = list(result_v3 = "result")}
model <- fit_logistic(
adrs,
variables = list(
response = "Response",
arm = "ARMCD",
covariates = c("SEX", "AGE", "RACE")
)
)
conf_level <- 0.95
df <- broom::tidy(model, conf_level = conf_level)
# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")
result <- basic_table() %>%
summarize_logistic(
conf_level = conf_level,
drop_and_remove_str = "_NA_"
) %>%
append_topleft("y ~ ARM + SEX + AGE + RACE") %>%
build_table(df = df)
result
```
```{r include = FALSE}
webr_code_labels <- c("variant3")
```
{{< include ../../_utils/webr.qmd >}}
:::
## Multi-Variable Logistic Regression Setting <br/> an Event, Alpha Level, and Level for Interaction
::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview
```{r variant4, test = list(result_v4 = "result")}
model <- fit_logistic(
adrs,
variables = list(
response = "Response",
arm = "ARMCD",
covariates = c("SEX", "AGE"),
interaction = "AGE"
),
response_definition = "1 - response"
)
conf_level <- 0.9
df <- broom::tidy(model, conf_level = conf_level, at = c(30, 50))
# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")
result <- basic_table() %>%
summarize_logistic(
conf_level = conf_level,
drop_and_remove_str = "_NA_"
) %>%
append_topleft("Estimations at age 30 and 50") %>%
build_table(df = df)
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
```{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
ADRS <- random.cdisc.data::cadrs %>%
filter(PARAMCD %in% c("BESRSPI", "INVET"))
})
datanames <- c("ADSL", "ADRS")
names(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]
## Reusable Configuration For Modules
ADRS <- data[["ADRS"]]
arm_ref_comp <- list(
ACTARMCD = list(
ref = "ARM B",
comp = c("ARM A", "ARM C")
),
ARM = list(
ref = "B: Placebo",
comp = c("A: Drug X", "C: Combination")
)
)
## Setup App
app <- init(
data = data,
modules = modules(
tm_t_logistic(
label = "Logistic Regression",
dataname = "ADRS",
arm_var = choices_selected(
choices = variable_choices(ADRS, c("ARM", "ARMCD")),
selected = "ARM"
),
arm_ref_comp = arm_ref_comp,
paramcd = choices_selected(
choices = value_choices(ADRS, "PARAMCD", "PARAM"),
selected = "BESRSPI"
),
cov_var = choices_selected(
choices = c("SEX", "AGE", "BMRKR1", "BMRKR2"),
selected = "SEX"
)
)
)
)
shinyApp(app$ui, app$server)
```
{{< include ../../_utils/shinylive.qmd >}}
:::
{{< include ../../repro.qmd >}}
