Package 'teal.osprey'

Automatically switch variable labels for standard AE variables in AE osprey functions

Description

Automatically switch variable labels for standard AE variables in AE osprey functions

Usage

label_aevar(x)

Arguments

x	variable key

---

Helper function to plot decorated output UI

Description

Usage

plot_decorate_output(id)

Arguments

id	(character) id of this element

---

Utility function for quick filter

Description

Utility function for quick filter

Usage

quick_filter(filter_opt, ANL)

Arguments

filter_opt	vector of string names of flag variable to filter (keep Y rows only)
ANL	input dataset

Value

a filtered dataframe

Author(s)

Carolyn Zhang (zhanc107) [email protected]

---

Helper server function to decorate plot output

Description

This is used in tm_g_ae_oview and tm_g_events_term_id.

Usage

srv_g_decorate(
  id,
  plot_id = "out",
  plt = reactive(NULL),
  plot_height,
  plot_width
)

Arguments

id	(character) id of the module
plot_id	(character) id for plot output
plt	(reactive) a reactive object of graph object
plot_height	optional, (numeric) a vector of length three with c(value, min, max). Specifies the height of the main plot.
plot_width	optional, (numeric) a vector of length three with c(value, min, max). Specifies the width of the main plot and renders a slider on the plot to interactively adjust the plot width.

---

Teal module for the AE overview

Description

Display the AE overview plot as a shiny module

Usage

tm_g_ae_oview(
  label,
  dataname,
  arm_var,
  flag_var_anl,
  fontsize = c(5, 3, 7),
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname	(character(1)) analysis data used in the teal module, needs to be available in the list passed to the data argument of teal::init().
arm_var	(choices_selected) object with all available choices and the pre-selected option for variable names that can be used as arm_var. See teal.transform::choices_selected() for details. Column arm_var in the dataname has to be a factor.
flag_var_anl	(teal.transform::choices_selected) choices_selected object with variables used to count adverse event sub-groups (e.g. Serious events, Related events, etc.)
fontsize	(numeric(1) or numeric(3)) Defines initial possible range of font-size. fontsize is set for teal.widgets::optionalSliderInputValMinMax() which controls font-size in the output plot.
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.

Value

the teal::module() object.

Examples

library(nestcolor)

ADSL <- osprey::rADSL
ADAE <- osprey::rADAE

# Add additional dummy causality flags.
add_event_flags <- function(dat) {
  dat <- dat %>%
    dplyr::mutate(
      TMPFL_SER = AESER == "Y",
      TMPFL_REL = AEREL == "Y",
      TMPFL_GR5 = AETOXGR == "5",
      AEREL1 = (AEREL == "Y" & ACTARM == "A: Drug X"),
      AEREL2 = (AEREL == "Y" & ACTARM == "B: Placebo")
    )
  labels <- c(
    "Serious AE", "Related AE", "Grade 5 AE",
    "AE related to A: Drug X", "AE related to B: Placebo"
  )
  cols <- c("TMPFL_SER", "TMPFL_REL", "TMPFL_GR5", "AEREL1", "AEREL2")
  for (i in seq_along(labels)) {
    attr(dat[[cols[i]]], "label") <- labels[i]
  }
  dat
}
ADAE <- ADAE %>% add_event_flags()

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- osprey::rADSL"),
    cdisc_dataset("ADAE", ADAE,
      code =
        "ADAE <- osprey::rADAE
           add_event_flags <- function(dat) {
             dat <- dat %>%
               dplyr::mutate(
                 TMPFL_SER = AESER == 'Y',
                 TMPFL_REL = AEREL == 'Y',
                 TMPFL_GR5 = AETOXGR == '5',
                 AEREL1 = (AEREL == 'Y' & ACTARM == 'A: Drug X'),
                 AEREL2 = (AEREL == 'Y' & ACTARM == 'B: Placebo')
               )
             labels <- c(
               'Serious AE',
               'Related AE',
               'Grade 5 AE',
               'AE related to A: Drug X',
               'AE related to B: Placebo'
             )
             cols <- c('TMPFL_SER', 'TMPFL_REL', 'TMPFL_GR5', 'AEREL1', 'AEREL2')
             for (i in seq_along(labels)) {
              attr(dat[[cols[i]]], 'label') <- labels[i]
             }
             dat
           }
           # Generating user-defined event flags.
           ADAE <- ADAE %>% add_event_flags()"
    ),
    check = TRUE
  ),
  modules = modules(
    tm_g_ae_oview(
      label = "AE Overview",
      dataname = "ADAE",
      arm_var = teal.transform::choices_selected(
        selected = "ACTARM",
        choices = c("ACTARM", "ACTARMCD")
      ),
      flag_var_anl = teal.transform::choices_selected(
        selected = "AEREL1",
        choices = teal.transform::variable_choices(
          ADAE,
          c("TMPFL_SER", "TMPFL_REL", "TMPFL_GR5", "AEREL1", "AEREL2")
        ),
      ),
      plot_height = c(600, 200, 2000)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

teal module for the AE by subgroups

Description

Display the AE by subgroups plot as a teal module

Usage

tm_g_ae_sub(
  label,
  dataname,
  arm_var,
  group_var,
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL,
  fontsize = c(5, 3, 7)
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname	(character(1)) analysis data used in the teal module, needs to be available in the list passed to the data argument of teal::init().
arm_var	(choices_selected) object with all available choices and the pre-selected option for variable names that can be used as arm_var. See teal.transform::choices_selected() for details. Column arm_var in the dataname has to be a factor.
group_var	(choices_selected) subgroups variables. See teal.transform::choices_selected() for details.
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.
fontsize	(numeric(1) or numeric(3)) Defines initial possible range of font-size. fontsize is set for teal.widgets::optionalSliderInputValMinMax() which controls font-size in the output plot.

Value

the teal::module() object.

Author(s)

Liming Li (Lil128) [email protected]

Molly He (hey59) [email protected]

Examples

# Example using stream (ADaM) dataset
ADSL <- osprey::rADSL
ADAE <- osprey::rADAE

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- osprey::rADSL"),
    cdisc_dataset("ADAE", ADAE, code = "ADAE <- osprey::rADAE"),
    check = TRUE
  ),
  modules = modules(
    tm_g_ae_sub(
      label = "AE by Subgroup",
      dataname = "ADAE",
      arm_var = teal.transform::choices_selected(
        selected = "ACTARMCD",
        choices = c("ACTARM", "ACTARMCD")
      ),
      group_var = teal.transform::choices_selected(
        selected = c("SEX", "REGION1", "RACE"),
        choices = c("SEX", "REGION1", "RACE")
      ),
      plot_height = c(600, 200, 2000)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Butterfly plot Teal Module

Description

Display butterfly plot as a shiny module

Usage

tm_g_butterfly(
  label,
  dataname,
  filter_var = NULL,
  right_var,
  left_var,
  category_var,
  color_by_var,
  count_by_var,
  facet_var = NULL,
  sort_by_var = teal.transform::choices_selected(selected = "count", choices = c("count",
    "alphabetical")),
  legend_on = TRUE,
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname	(character(1)) analysis data used in the teal module, needs to be available in the list passed to the data argument of teal::init().
filter_var	(choices_selected) variable name of data filter, please see details regarding expected values, default is NULL. choices vector with filter_var choices, default is NULL
right_var	(choices_selected) dichotomization variable for right side
left_var	(choices_selected) dichotomization variable for left side
category_var	(choices_selected) category (y axis) variable
color_by_var	(choices_selected) variable defines color blocks within each bar
count_by_var	(choices_selected) variable defines how x axis is calculated
facet_var	(choices_selected) variable for row facets
sort_by_var	(choices_selected) argument for order of class and term elements in table, default here is "count"
legend_on	(boolean) value for whether legend is displayed
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.
pre_output	(shiny.tag, optional) with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag, optional) with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

Details

filter_var option is designed to work in conjunction with filtering function provided by teal (encoding panel on the right hand side of the shiny app). It can be used as quick access to predefined subsets of the domain datasets (not subject-level dataset) to be used for analysis, denoted by an value of "Y". Each variable within the filter_var_choices is expected to contain values of either "Y" or "N". If multiple variables are selected as filter_var, only observations with "Y" value in each and every selected variables will be used for subsequent analysis. Flag variables (from ADaM datasets) can be used directly as filter.

Value

the teal::module() object.

Author(s)

Carolyn Zhang (zhanc107) [email protected]

Chendi Liao (liaoc10) [email protected]

Examples

# Example using stream (ADaM) dataset
library(dplyr)
library(nestcolor)

set.seed(23)
ADSL <- osprey::rADSL
ADAE <- osprey::rADAE
ADSL <- mutate(ADSL, DOSE = paste(sample(1:3, n(), replace = TRUE), "UG"))
ADAE <- mutate(
  ADAE,
  flag1 = ifelse(AETOXGR == 1, 1, 0),
  flag2 = ifelse(AETOXGR == 2, 1, 0),
  flag3 = ifelse(AETOXGR == 3, 1, 0),
  flag1_filt = rep("Y", n())
)

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL,
      code = "ADSL <- osprey::rADSL
              set.seed(23)
              ADSL <- mutate(ADSL, DOSE = paste(sample(1:3, n(), replace = TRUE), 'UG'))"
    ),
    cdisc_dataset("ADAE", ADAE,
      code = "ADAE <- osprey::rADAE
              ADAE <- mutate(ADAE,
              flag1 = ifelse(AETOXGR == 1, 1, 0),
              flag2 = ifelse(AETOXGR == 2, 1, 0),
              flag3 = ifelse(AETOXGR == 3, 1, 0),
              flag1_filt = rep('Y', n()))"
    ),
    check = TRUE
  ),
  modules = modules(
    tm_g_butterfly(
      label = "Butterfly Plot",
      dataname = "ADAE",
      right_var = teal.transform::choices_selected(
        selected = "SEX",
        choices = c("SEX", "ARM", "RACE")
      ),
      left_var = teal.transform::choices_selected(
        selected = "RACE",
        choices = c("SEX", "ARM", "RACE")
      ),
      category_var = teal.transform::choices_selected(
        selected = "AEBODSYS",
        choices = c("AEDECOD", "AEBODSYS")
      ),
      color_by_var = teal.transform::choices_selected(
        selected = "AETOXGR",
        choices = c("AETOXGR", "None")
      ),
      count_by_var = teal.transform::choices_selected(
        selected = "# of patients",
        choices = c("# of patients", "# of AEs")
      ),
      facet_var = teal.transform::choices_selected(
        selected = NULL,
        choices = c("RACE", "SEX", "ARM")
      ),
      sort_by_var = teal.transform::choices_selected(
        selected = "count",
        choices = c("count", "alphabetical")
      ),
      legend_on = TRUE,
      plot_height = c(600, 200, 2000)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Events by Term Plot Teal Module

Description

Display Events by Term plot as a shiny module

Usage

tm_g_events_term_id(
  label,
  dataname,
  term_var,
  arm_var,
  fontsize = c(5, 3, 7),
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname	(character(1)) analysis data used in the teal module, needs to be available in the list passed to the data argument of teal::init().
term_var	choices_selected object with all available choices and pre-selected option names that can be used to specify the term for events
arm_var	(choices_selected) object with all available choices and the pre-selected option for variable names that can be used as arm_var. See teal.transform::choices_selected() for details. Column arm_var in the dataname has to be a factor.
fontsize	(numeric(1) or numeric(3)) Defines initial possible range of font-size. fontsize is set for teal.widgets::optionalSliderInputValMinMax() which controls font-size in the output plot.
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.

Value

the teal::module() object.

Author(s)

Liming Li (lil128) [email protected]

Molly He (hey59) [email protected]

Examples

library(nestcolor)

ADSL <- osprey::rADSL
ADAE <- osprey::rADAE

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- osprey::rADSL"),
    cdisc_dataset("ADAE", ADAE, code = "ADAE <- osprey::rADAE"),
    check = TRUE
  ),
  modules = modules(
    tm_g_events_term_id(
      label = "Common AE",
      dataname = "ADAE",
      term_var = teal.transform::choices_selected(
        selected = "AEDECOD",
        choices = c(
          "AEDECOD", "AETERM",
          "AEHLT", "AELLT", "AEBODSYS"
        )
      ),
      arm_var = teal.transform::choices_selected(
        selected = "ACTARMCD",
        choices = c("ACTARM", "ACTARMCD")
      ),
      plot_height = c(600, 200, 2000)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Teal module for the heatmap by grade

Description

Display the heatmap by grade as a shiny module

Usage

tm_g_heat_bygrade(
  label,
  sl_dataname,
  ex_dataname,
  ae_dataname,
  cm_dataname = NA,
  id_var,
  visit_var,
  ongo_var,
  anno_var,
  heat_var,
  conmed_var = NULL,
  fontsize = c(5, 3, 7),
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
sl_dataname	(character) subject level dataset name, needs to be available in the list passed to the data argument of init
ex_dataname	(character) exposures dataset name, needs to be available in the list passed to the data argument of init
ae_dataname	(character) adverse events dataset name, needs to be available in the list passed to the data argument of init
cm_dataname	(character) concomitant medications dataset name, needs to be available in the list passed to the data argument of init specify to NA if no concomitant medications data is available
id_var	(choices_seleced) unique subject ID variable
visit_var	(choices_seleced) analysis visit variable
ongo_var	(choices_seleced) study ongoing status variable, This variable is a derived logical variable. Usually it can be derived from EOSSTT.
anno_var	(choices_seleced) annotation variable
heat_var	(choices_seleced) heatmap variable
conmed_var	(choices_seleced) concomitant medications variable, specify to NA if no concomitant medications data is available
fontsize	(numeric(1) or numeric(3)) Defines initial possible range of font-size. fontsize is set for teal.widgets::optionalSliderInputValMinMax() which controls font-size in the output plot.
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.

Value

the teal::module() object.

Examples

library(dplyr)
library(nestcolor)
ADSL <- osprey::rADSL %>% slice(1:30)
ADEX <- osprey::rADEX %>% filter(USUBJID %in% ADSL$USUBJID)
ADAE <- osprey::rADAE %>% filter(USUBJID %in% ADSL$USUBJID)
ADCM <- osprey::rADCM %>% filter(USUBJID %in% ADSL$USUBJID)

# This preprocess is only to force legacy standard on ADCM
ADCM <- ADCM %>%
  select(-starts_with("ATC")) %>%
  unique()

# function to derive AVISIT from ADEX
add_visit <- function(data_need_visit) {
  visit_dates <- ADEX %>%
    filter(PARAMCD == "DOSE") %>%
    distinct(USUBJID, AVISIT, ASTDTM) %>%
    group_by(USUBJID) %>%
    arrange(ASTDTM) %>%
    mutate(next_vis = lead(ASTDTM), is_last = ifelse(is.na(next_vis), TRUE, FALSE)) %>%
    rename(this_vis = ASTDTM)
  data_visit <- data_need_visit %>%
    select(USUBJID, ASTDTM) %>%
    left_join(visit_dates, by = "USUBJID") %>%
    filter(ASTDTM > this_vis & (ASTDTM < next_vis | is_last == TRUE)) %>%
    left_join(data_need_visit) %>%
    distinct()
  return(data_visit)
}
# derive AVISIT for ADAE and ADCM
ADAE <- add_visit(ADAE)
ADCM <- add_visit(ADCM)
# derive ongoing status variable for ADEX
ADEX <- ADEX %>%
  filter(PARCAT1 == "INDIVIDUAL") %>%
  mutate(ongo_status = (EOSSTT == "ONGOING"))

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL),
    cdisc_dataset("ADEX", ADEX),
    cdisc_dataset("ADAE", ADAE),
    cdisc_dataset("ADCM", ADCM, keys = c("STUDYID", "USUBJID", "ASTDTM", "CMSEQ", "CMDECOD")),
    code = "
    ADSL <- osprey::rADSL %>% slice(1:30)
    ADEX <- osprey::rADEX %>% filter(USUBJID %in% ADSL$USUBJID)
    ADAE <- osprey::rADAE %>% filter(USUBJID %in% ADSL$USUBJID)
    ADCM <- osprey::rADCM %>% filter(USUBJID %in% ADSL$USUBJID)
    ADCM <- ADCM %>% select(-starts_with(\"ATC\")) %>% unique()
    ADEX  <- ADEX %>%
      filter(PARCAT1 == 'INDIVIDUAL') %>%
      mutate(ongo_status = (EOSSTT == 'ONGOING'))
    add_visit <- function(data_need_visit) {
      visit_dates <- ADEX %>%
        filter(PARAMCD == 'DOSE') %>%
        distinct(USUBJID, AVISIT, ASTDTM) %>%
        group_by(USUBJID) %>%
        arrange(ASTDTM) %>%
        mutate(next_vis = lead(ASTDTM), is_last = ifelse(is.na(next_vis), TRUE, FALSE)) %>%
        rename(this_vis = ASTDTM)
      data_visit <- data_need_visit %>%
        select(USUBJID, ASTDTM) %>%
        left_join(visit_dates, by = 'USUBJID') %>%
        filter(ASTDTM > this_vis & (ASTDTM < next_vis | is_last == TRUE)) %>%
        left_join(data_need_visit) %>% distinct()
      return(data_visit)
    }
    ADAE <- add_visit(ADAE)
    ADCM <- add_visit(ADCM)
    ",
    check = TRUE
  ),
  modules = modules(
    tm_g_heat_bygrade(
      label = "Heatmap by grade",
      sl_dataname = "ADSL",
      ex_dataname = "ADEX",
      ae_dataname = "ADAE",
      cm_dataname = "ADCM",
      id_var = teal.transform::choices_selected(
        selected = "USUBJID",
        choices = c("USUBJID", "SUBJID")
      ),
      visit_var = teal.transform::choices_selected(
        selected = "AVISIT",
        choices = c("AVISIT")
      ),
      ongo_var = teal.transform::choices_selected(
        selected = "ongo_status",
        choices = c("ongo_status")
      ),
      anno_var = teal.transform::choices_selected(
        selected = c("SEX", "COUNTRY"),
        choices = c("SEX", "COUNTRY", "USUBJID")
      ),
      heat_var = teal.transform::choices_selected(
        selected = "AETOXGR",
        choices = c("AETOXGR")
      ),
      conmed_var = teal.transform::choices_selected(
        selected = "CMDECOD",
        choices = c("CMDECOD")
      ),
      plot_height = c(600, 200, 2000)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Patient Profile plot teal module

Description

Display patient profile plot as a shiny module

Usage

tm_g_patient_profile(
  label = "Patient Profile Plot",
  patient_id,
  sl_dataname,
  ex_dataname = NA,
  ae_dataname = NA,
  rs_dataname = NA,
  cm_dataname = NA,
  lb_dataname = NA,
  sl_start_date,
  ex_var = NULL,
  ae_var = NULL,
  ae_line_col_var = NULL,
  ae_line_col_opt = NULL,
  rs_var = NULL,
  cm_var = NULL,
  lb_var = NULL,
  x_limit = "-28, 365",
  plot_height = c(1200L, 400L, 5000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
patient_id	(choices_seleced) unique subject ID variable
sl_dataname	(character) subject level dataset name, needs to be available in the list passed to the data argument of init
ex_dataname, ae_dataname, rs_dataname, cm_dataname, lb_dataname	(character(1)) names of exposure, adverse events, response, concomitant medications, and labs datasets, respectively; must be available in the list passed to the data argument of init set to NA (default) to omit from analysis
sl_start_date	(choices_selected) study start date variable, usually set to treatment start date or randomization date
ex_var	(choices_selected) exposure variable to plot as each line leave unspecified or set to NULL if exposure data is not available
ae_var	(choices_selected) adverse event variable to plot as each line leave unspecified or set to NULL if adverse events data is not available
ae_line_col_var	(choices_selected) variable for coloring AE lines leave unspecified or set to NULL if adverse events data is not available
ae_line_col_opt	aesthetic values to map color values (named vector to map color values to each name). If not NULL, please make sure this contains all possible values for ae_line_col_var values. leave unspecified or set to NULL if adverse events data is not available
rs_var	(choices_selected) response variable to plot as each line leave unspecified or set to NULL if response data is not available
cm_var	(choices_selected) concomitant medication variable to plot as each line leave unspecified or set to NULL if concomitant medications data is not available
lb_var	(choices_selected) lab variable to plot as each line leave unspecified or set to NULL if labs data is not available
x_limit	a single character string with two numbers separated by a comma indicating the x-axis limit, default is "-28, 365"
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.
pre_output	(shiny.tag, optional) with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag, optional) with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

Details

As the patient profile module plots different domains in one plot, the study day (x-axis) is derived for consistency based the start date of user's choice in the app (for example, ADSL.RANDDT or ADSL.TRTSDT):

• In ADAE, ADEX, and ADCM, it would be study day based on ASTDT and/or AENDT in reference to the start date

• In ADRS and ADLB, it would be study day based on ADT in reference to the start date

Value

the teal::module() object.

Author(s)

Xuefeng Hou (houx14) [email protected]

Tina Cho (chot) [email protected]

Molly He (hey59) [email protected]

Ting Qi (qit3) [email protected]

Examples

library(nestcolor)

ADSL <- osprey::rADSL
ADAE <- osprey::rADAE %>%
  mutate(
    ASTDT = as.Date(ASTDTM),
    AENDT = as.Date(AENDTM)
  )
ADCM <- osprey::rADCM %>%
  mutate(
    ASTDT = as.Date(ASTDTM),
    AENDT = as.Date(AENDTM)
  )

# The step below is to pre-process ADCM to legacy standard
ADCM <- ADCM %>%
  select(-starts_with("ATC")) %>%
  unique()

ADRS <- osprey::rADRS %>%
  mutate(ADT = as.Date(ADTM))
ADEX <- osprey::rADEX %>%
  mutate(
    ASTDT = as.Date(ASTDTM),
    AENDT = as.Date(AENDTM)
  )
ADLB <- osprey::rADLB %>%
  mutate(
    ADT = as.Date(ADTM),
    LBSTRESN = as.numeric(LBSTRESC)
  )

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- osprey::rADSL"),
    cdisc_dataset("ADRS", ADRS, code = "ADRS <- osprey::rADRS %>% mutate(ADT = as.Date(ADTM))"),
    cdisc_dataset("ADAE", ADAE,
      code = "ADAE <- osprey::rADAE %>%
              mutate(ASTDT = as.Date(ASTDTM),
                     AENDT = as.Date(AENDTM))"
    ),
    cdisc_dataset("ADCM", ADCM,
      code = "ADCM <- osprey::rADCM %>%
              mutate(ASTDT = as.Date(ASTDTM),
                     AENDT = as.Date(AENDTM))
              ADCM <- ADCM %>% select(-starts_with(\"ATC\")) %>% unique()",
      keys = c("STUDYID", "USUBJID", "ASTDTM", "CMSEQ", "CMDECOD")
    ),
    cdisc_dataset("ADLB", ADLB,
      code = "ADLB <- osprey::rADLB %>%
              mutate(ADT = as.Date(ADTM),
                     LBSTRESN = as.numeric(LBSTRESC))"
    ),
    cdisc_dataset("ADEX", ADEX,
      code = "ADEX <- osprey::rADEX %>%
              mutate(ASTDT = as.Date(ASTDTM),
                     AENDT = as.Date(AENDTM))"
    ),
    check = FALSE # set FALSE here to keep run time of example short, should be set to TRUE
  ),
  modules = modules(
    tm_g_patient_profile(
      label = "Patient Profile Plot",
      patient_id = teal.transform::choices_selected(
        choices = unique(ADSL$USUBJID),
        selected = unique(ADSL$USUBJID)[1]
      ),
      sl_dataname = "ADSL",
      ex_dataname = "ADEX",
      ae_dataname = "ADAE",
      rs_dataname = "ADRS",
      cm_dataname = "ADCM",
      lb_dataname = "ADLB",
      sl_start_date = teal.transform::choices_selected(
        selected = "TRTSDTM",
        choices = c("TRTSDTM", "RANDDT")
      ),
      ex_var = teal.transform::choices_selected(
        selected = "PARCAT2",
        choices = "PARCAT2"
      ),
      ae_var = teal.transform::choices_selected(
        selected = "AEDECOD",
        choices = c("AEDECOD", "AESOC")
      ),
      ae_line_col_var = teal.transform::choices_selected(
        selected = "AESER",
        choices = c("AESER", "AEREL")
      ),
      ae_line_col_opt = c("Y" = "red", "N" = "blue"),
      rs_var = teal.transform::choices_selected(
        selected = "PARAMCD",
        choices = "PARAMCD"
      ),
      cm_var = teal.transform::choices_selected(
        selected = "CMDECOD",
        choices = c("CMDECOD", "CMCAT")
      ),
      lb_var = teal.transform::choices_selected(
        selected = "LBTESTCD",
        choices = c("LBTESTCD", "LBCAT")
      ),
      x_limit = "-28, 750",
      plot_height = c(1200, 400, 5000)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Spider plot Teal Module

Description

Display spider plot as a shiny module

Usage

tm_g_spiderplot(
  label,
  dataname,
  paramcd,
  x_var,
  y_var,
  marker_var,
  line_colorby_var,
  xfacet_var = NULL,
  yfacet_var = NULL,
  vref_line = NULL,
  href_line = NULL,
  anno_txt_var = TRUE,
  legend_on = FALSE,
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname	(character(1)) analysis data used in the teal module, needs to be available in the list passed to the data argument of teal::init().
paramcd	(character(1) or choices_selected) variable value designating the studied parameter. See teal.transform::choices_selected() for details.
x_var	x-axis variables
y_var	y-axis variables
marker_var	variable dictates marker symbol
line_colorby_var	variable dictates line color
xfacet_var	variable for x facets
yfacet_var	variable for y facets
vref_line	vertical reference lines
href_line	horizontal reference lines
anno_txt_var	annotation text
legend_on	boolean value for whether legend is displayed
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.
pre_output	(shiny.tag, optional) with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag, optional) with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

Value

the teal::module() object.

Author(s)

Carolyn Zhang (zhanc107) [email protected]

Chendi Liao (liaoc10) [email protected]

Examples

# Example using stream (ADaM) dataset
library(dplyr)
library(nestcolor)

ADSL <- osprey::rADSL
ADTR <- osprey::rADTR

app <- teal::init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- osprey::rADSL"),
    cdisc_dataset("ADTR", ADTR,
      code = "ADTR <- osprey::rADTR",
      keys = c("STUDYID", "USUBJID", "PARAMCD", "AVISIT")
    ),
    check = TRUE
  ),
  modules = modules(
    tm_g_spiderplot(
      label = "Spider plot",
      dataname = "ADTR",
      paramcd = teal.transform::choices_selected(
        choices = "SLDINV",
        selected = "SLDINV"
      ),
      x_var = teal.transform::choices_selected(
        choices = "ADY",
        selected = "ADY"
      ),
      y_var = teal.transform::choices_selected(
        choices = c("PCHG", "CHG", "AVAL"),
        selected = "PCHG"
      ),
      marker_var = teal.transform::choices_selected(
        choices = c("SEX", "RACE", "USUBJID"),
        selected = "SEX"
      ),
      line_colorby_var = teal.transform::choices_selected(
        choices = c("SEX", "USUBJID", "RACE"),
        selected = "SEX"
      ),
      xfacet_var = teal.transform::choices_selected(
        choices = c("SEX", "ARM"),
        selected = "SEX"
      ),
      yfacet_var = teal.transform::choices_selected(
        choices = c("SEX", "ARM"),
        selected = "ARM"
      ),
      vref_line = "10, 37",
      href_line = "-20, 0"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Teal Module for Swimlane Plot

Description

This is teal module that generates a swimlane plot (bar plot with markers) for ADaM data

Usage

tm_g_swimlane(
  label,
  dataname,
  bar_var,
  bar_color_var = NULL,
  sort_var = NULL,
  marker_pos_var = NULL,
  marker_shape_var = NULL,
  marker_shape_opt = NULL,
  marker_color_var = NULL,
  marker_color_opt = NULL,
  anno_txt_var = NULL,
  vref_line = NULL,
  plot_height = c(1200L, 400L, 5000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  x_label = "Time from First Treatment (Day)"
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname	analysis data used for plotting, needs to be available in the list passed to the data argument of init. If no markers are to be plotted in the module, "ADSL" should be the input. If markers are to be plotted, data name for the marker data should be the input
bar_var	(choices_selected) subject-level numeric variable from dataset to plot as the bar length
bar_color_var	(choices_selected) color by variable (subject-level)
sort_var	(choices_selected) sort by variable (subject-level)
marker_pos_var	(choices_selected) variable for marker position from marker data (Note: make sure that marker position has the same relative start day as bar length variable bar_var)
marker_shape_var	(choices_selected) marker shape variable from marker data
marker_shape_opt	aesthetic values to map shape values (named vector to map shape values to each name). If not NULL, please make sure this contains all possible values for marker_shape_var values, otherwise shape will be assigned by ggplot default
marker_color_var	marker color variable from marker data
marker_color_opt	aesthetic values to map color values (named vector to map color values to each name). If not NULL, please make sure this contains all possible values for marker_color_var values, otherwise color will be assigned by ggplot default
anno_txt_var	character vector with subject-level variable names that are selected as annotation
vref_line	vertical reference lines
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.
pre_output	(shiny.tag, optional) with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag, optional) with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.
x_label	the label of the x axis

Value

the teal::module() object.

Author(s)

Ting Qi (qit3) [email protected]

Examples

# Example using stream (ADaM) dataset
library(dplyr)
library(nestcolor)

ADSL <- osprey::rADSL %>%
  dplyr::mutate(TRTDURD = as.integer(TRTEDTM - TRTSDTM) + 1) %>%
  dplyr::filter(STRATA1 == "A" & ARMCD == "ARM A")
ADRS <- osprey::rADRS

ADRS <- ADRS %>%
  dplyr::filter(PARAMCD == "LSTASDI" & DCSREAS == "Death") %>%
  mutate(AVALC = DCSREAS, ADY = EOSDY) %>%
  base::rbind(ADRS %>% dplyr::filter(PARAMCD == "OVRINV" & AVALC != "NE")) %>%
  arrange(USUBJID)

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL, code = "ADSL <- osprey::rADSL %>%
      dplyr::mutate(TRTDURD = as.integer(TRTEDTM - TRTSDTM) + 1) %>%
      dplyr::filter(STRATA1 == 'A' & ARMCD == 'ARM A')"),
    cdisc_dataset("ADRS", ADRS,
      code = "ADRS <- rADRS
              ADRS <- ADRS %>% dplyr::filter(PARAMCD == 'LSTASDI' & DCSREAS == 'Death') %>%
                      mutate(AVALC = DCSREAS, ADY = EOSDY) %>%
              rbind(ADRS %>% dplyr::filter(PARAMCD == 'OVRINV' & AVALC != 'NE')) %>%
              arrange(USUBJID)"
    ),
    check = TRUE
  ),
  modules = modules(
    tm_g_swimlane(
      label = "Swimlane Plot",
      dataname = "ADRS",
      bar_var = teal.transform::choices_selected(
        selected = "TRTDURD",
        choices = c("TRTDURD", "EOSDY")
      ),
      bar_color_var = teal.transform::choices_selected(
        selected = "EOSSTT",
        choices = c("EOSSTT", "ARM", "ARMCD", "ACTARM", "ACTARMCD", "SEX")
      ),
      sort_var = teal.transform::choices_selected(
        selected = "ACTARMCD",
        choices = c("USUBJID", "SITEID", "ACTARMCD", "TRTDURD")
      ),
      marker_pos_var = teal.transform::choices_selected(
        selected = "ADY",
        choices = c("ADY")
      ),
      marker_shape_var = teal.transform::choices_selected(
        selected = "AVALC",
        c("AVALC", "AVISIT")
      ),
      marker_shape_opt = c("CR" = 16, "PR" = 17, "SD" = 18, "PD" = 15, "Death" = 8),
      marker_color_var = teal.transform::choices_selected(
        selected = "AVALC",
        choices = c("AVALC", "AVISIT")
      ),
      marker_color_opt = c(
        "CR" = "green", "PR" = "blue", "SD" = "goldenrod",
        "PD" = "red", "Death" = "black"
      ),
      vref_line = c(30, 60),
      anno_txt_var = teal.transform::choices_selected(
        selected = c("ACTARM", "SEX"),
        choices = c(
          "ARM", "ARMCD", "ACTARM", "ACTARMCD", "AGEGR1",
          "SEX", "RACE", "COUNTRY", "DCSREAS", "DCSREASP"
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Teal Module for Waterfall Plot

Description

This is teal module that generates a waterfall plot for ADaM data

Usage

tm_g_waterfall(
  label,
  dataname_tr = "ADTR",
  dataname_rs = "ADRS",
  bar_paramcd,
  bar_var,
  bar_color_var,
  bar_color_opt = NULL,
  sort_var,
  add_label_var_sl,
  add_label_paramcd_rs,
  anno_txt_var_sl,
  anno_txt_paramcd_rs,
  facet_var,
  ytick_at = 20,
  href_line = NULL,
  gap_point_val = NULL,
  show_value = TRUE,
  plot_height = c(1200L, 400L, 5000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL
)

Arguments

label	(character(1)) menu item label of the module in the teal app.
dataname_tr	tumor burden analysis data used in teal module to plot as bar height, needs to be available in the list passed to the data argument of init
dataname_rs	response analysis data used in teal module to label response parameters, needs to be available in the list passed to the data argument of init
bar_paramcd	(choices_selected) parameter in tumor burden data that will be plotted as bar height
bar_var	(choices_selected) numeric variable from dataset to plot the bar height, e.g., PCHG
bar_color_var	(choices_selected) color by variable (subject level), None corresponds to NULL
bar_color_opt	aesthetic values to map color values (named vector to map color values to each name). If not NULL, please make sure this contains all possible values for bar_color_var values, otherwise color will be assigned by ggplot default, please note that NULL needs to be specified in this case
sort_var	(choices_selected) sort by variable (subject level), None corresponds to NULL
add_label_var_sl	(choices_selected) add label to bars (subject level), None corresponds to NULL
add_label_paramcd_rs	(choices_selected) add label to bars (response dataset), None corresponds to NULL. At least one of add_label_var_sl and add_label_paramcd_rs needs to be NULL
anno_txt_var_sl	(choices_selected) subject level variables to be displayed in the annotation table, default is NULL
anno_txt_paramcd_rs	(choices_selected) analysis dataset variables to be displayed in the annotation table, default is NULL
facet_var	(choices_selected) facet by variable (subject level), None corresponds to NULL
ytick_at	bar height axis interval, default is 20
href_line	numeric vector to plot horizontal reference lines, default is NULL
gap_point_val	singular numeric value for adding bar break when some bars are significantly higher than others, default is NULL
show_value	boolean of whether value of bar height is shown, default is TRUE
plot_height	(numeric(3)) vector to indicate default value, minimum and maximum values.
plot_width	(numeric(3)) vector to indicate default value, minimum and maximum values.
pre_output	(shiny.tag, optional) with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag, optional) with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

Value

the teal::module() object.

Author(s)

Ting Qi (qit3) [email protected]

houx14 [email protected]

Examples

library(nestcolor)
ADSL <- osprey::rADSL
ADRS <- osprey::rADRS
ADTR <- osprey::rADTR

ADSL$SEX <- factor(ADSL$SEX, levels = unique(ADSL$SEX))

app <- teal::init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL,
      code = "ADSL <- rADSL
              ADSL$SEX <- factor(ADSL$SEX, levels = unique(ADSL$SEX))"
    ),
    cdisc_dataset("ADRS", ADRS, code = "ADRS <- rADRS"),
    cdisc_dataset("ADTR", ADTR,
      code = " ADTR <- rADTR",
      c("STUDYID", "USUBJID", "PARAMCD", "AVISIT")
    ),
    check = TRUE
  ),
  modules = modules(
    tm_g_waterfall(
      label = "Waterfall",
      dataname_tr = "ADTR",
      dataname_rs = "ADRS",
      bar_paramcd = teal.transform::choices_selected(c("SLDINV"), "SLDINV"),
      bar_var = teal.transform::choices_selected(c("PCHG", "AVAL"), "PCHG"),
      bar_color_var = teal.transform::choices_selected(c("ARMCD", "SEX"), "ARMCD"),
      bar_color_opt = NULL,
      sort_var = teal.transform::choices_selected(c("ARMCD", "SEX"), NULL),
      add_label_var_sl = teal.transform::choices_selected(c("SEX", "EOSDY"), NULL),
      add_label_paramcd_rs = teal.transform::choices_selected(c("BESRSPI", "OBJRSPI"), NULL),
      anno_txt_var_sl = teal.transform::choices_selected(c("SEX", "ARMCD", "BMK1", "BMK2"), NULL),
      anno_txt_paramcd_rs = teal.transform::choices_selected(c("BESRSPI", "OBJRSPI"), NULL),
      facet_var = teal.transform::choices_selected(c("SEX", "ARMCD", "STRATA1", "STRATA2"), NULL),
      href_line = "-30, 20"
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

---

Helper UI function to decorate plot output UI

Description

This is used in tm_g_ae_oview and tm_g_events_term_id.

Usage

ui_g_decorate(
  id,
  titles = "Titles",
  footnotes = "footnotes",
  fontsize = c(5, 4, 11)
)

Arguments

id	(character) id of this module. set to NULL if you want to make it identical to the module who called it.
titles	(character) default titles
footnotes	(character) default footnotes
fontsize	(numeric(1) or numeric(3)) Defines initial possible range of font-size. fontsize is set for teal.widgets::optionalSliderInputValMinMax() which controls font-size in the output plot.

---