TLG Catalog - Stable
  • Stable
    • Dev
  1. Tables
  2. Exposure
  3. EXT01
  • Introduction

  • Tables
    • ADA
      • ADAT01
      • ADAT02
      • ADAT03
      • ADAT04A
      • ADAT04B
    • Adverse Events
      • AET01
      • AET01_AESI
      • AET02
      • AET02_SMQ
      • AET03
      • AET04
      • AET04_PI
      • AET05
      • AET05_ALL
      • AET06
      • AET06_SMQ
      • AET07
      • AET09
      • AET09_SMQ
      • AET10
    • Concomitant Medications
      • CMT01
      • CMT01A
      • CMT01B
      • CMT02_PT
    • Deaths
      • DTHT01
    • Demography
      • DMT01
    • Disclosures
      • DISCLOSUREST01
      • EUDRAT01
      • EUDRAT02
    • Disposition
      • DST01
      • PDT01
      • PDT02
    • ECG
      • EGT01
      • EGT02
      • EGT03
      • EGT04
      • EGT05_QTCAT
    • Efficacy
      • AOVT01
      • AOVT02
      • AOVT03
      • CFBT01
      • CMHT01
      • COXT01
      • COXT02
      • DORT01
      • LGRT02
      • MMRMT01
      • ONCT05
      • RATET01
      • RBMIT01
      • RSPT01
      • TTET01
    • Exposure
      • EXT01
    • Lab Results
      • LBT01
      • LBT02
      • LBT03
      • LBT04
      • LBT05
      • LBT06
      • LBT07
      • LBT08
      • LBT09
      • LBT10
      • LBT10_BL
      • LBT11
      • LBT11_BL
      • LBT12
      • LBT12_BL
      • LBT13
      • LBT14
      • LBT15
    • Medical History
      • MHT01
    • Pharmacokinetic
      • PKCT01
      • PKPT02
      • PKPT03
      • PKPT04
      • PKPT05
      • PKPT06
      • PKPT07
      • PKPT08
      • PKPT11
    • Risk Management Plan
      • RMPT01
      • RMPT03
      • RMPT04
      • RMPT05
      • RMPT06
    • Safety
      • ENTXX
    • Vital Signs
      • VST01
      • VST02
  • Listings
    • ADA
      • ADAL02
    • Adverse Events
      • AEL01
      • AEL01_NOLLT
      • AEL02
      • AEL02_ED
      • AEL03
      • AEL04
    • Concomitant Medications
      • CML01
      • CML02A_GL
      • CML02B_GL
    • Development Safety Update Report
      • DSUR4
    • Disposition
      • DSL01
      • DSL02
    • ECG
      • EGL01
    • Efficacy
      • ONCL01
    • Exposure
      • EXL01
    • Lab Results
      • LBL01
      • LBL01_RLS
      • LBL02A
      • LBL02A_RLS
      • LBL02B
    • Medical History
      • MHL01
    • Pharmacokinetic
      • ADAL01
      • PKCL01
      • PKCL02
      • PKPL01
      • PKPL02
      • PKPL04
    • Vital Signs
      • VSL01
  • Graphs
    • Efficacy
      • FSTG01
      • FSTG02
      • KMG01
      • MMRMG01
      • MMRMG02
    • Other
      • BRG01
      • BWG01
      • CIG01
      • IPPG01
      • LTG01
      • MNG01
    • Pharmacokinetic
      • PKCG01
      • PKCG02
      • PKCG03
      • PKPG01
      • PKPG02
      • PKPG03
      • PKPG04
      • PKPG06

  • Appendix
    • Reproducibility

  • Index

On this page

  • Output
  • teal App
  • Reproducibility
    • Timestamp
    • Session Info
    • .lock file
  • Edit this page
  • Report an issue
  1. Tables
  2. Exposure
  3. EXT01

EXT01

Study Drug Exposure Table


Output

  • Standard Table
  • Table with Optional Analyses
  • Table with User-Specified
    Categories for Missed Doses
  • Data Setup
  • Preview
  • Try this using WebR
Code
# When summary table contains only categorical or only numeric parameters

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  split_rows_by("PARAM", split_fun = split_fun) %>%
  analyze_vars(vars = "AVAL")

result <- build_table(lyt = lyt, df = adex, alt_counts_df = adsl)

result
                                                 A: Drug X        B: Placebo      C: Combination 
Parameter Category (Drug A/Drug B)                (N=134)           (N=134)           (N=132)    
—————————————————————————————————————————————————————————————————————————————————————————————————
Drug A                                                                                           
  Overall duration (days)                                                                        
    n                                               75                67                75       
    Mean (SD)                                   74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  
    Median                                         77.0              80.0              78.0      
    Min - Max                                   5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  
  Total dose administered                                                                        
    n                                               75                67                75       
    Mean (SD)                                 6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)
    Median                                        6720.0            6480.0            7200.0     
    Min - Max                                 4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0
  Total number of doses administered                                                             
    n                                               75                67                75       
    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                          7.0               7.0               7.0      
    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Total number of missed doses during study                                                      
    n                                               75                67                75       
    Mean (SD)                                   10.5 (5.9)        10.0 (6.1)         9.5 (5.5)   
    Median                                         10.0              11.0               9.0      
    Min - Max                                   0.0 - 20.0        0.0 - 19.0        0.0 - 20.0   
Drug B                                                                                           
  Overall duration (days)                                                                        
    n                                               59                67                57       
    Mean (SD)                                   77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  
    Median                                         79.0              76.0              65.0      
    Min - Max                                   2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  
  Total dose administered                                                                        
    n                                               59                67                57       
    Mean (SD)                                 6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)
    Median                                        6720.0            6240.0            6480.0     
    Min - Max                                 4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0
  Total number of doses administered                                                             
    n                                               59                67                57       
    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                          7.0               7.0               7.0      
    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Total number of missed doses during study                                                      
    n                                               59                67                57       
    Mean (SD)                                    9.1 (6.5)        10.0 (6.3)        10.7 (5.8)   
    Median                                          9.0              10.0              11.0      
    Min - Max                                   0.0 - 20.0        0.0 - 20.0        1.0 - 20.0   
Experimental use!

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Code
# When summary table contains both categorical and numeric parameters,
# developer needs to do pre-processing to transform dataset to wide format.
adex_avalc_wide <- adex %>%
  filter(PARAMCD == "TDURD") %>%
  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, PARCAT2),
    names_from = PARAMCD,
    values_from = AVALC
  ) %>%
  mutate(
    TDURDC = factor(TDURD, levels = c("0 - 30", "31 - 60", "61 - 90", ">= 91"))
  ) %>%
  select(-TDURD)

anl <- adex %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),
    names_from = PARAMCD,
    values_from = AVAL
  ) %>%
  left_join(adex_avalc_wide, by = c("STUDYID", "USUBJID", "PARCAT2")) %>%
  var_relabel(
    TDOSE = "Total dose administered",
    TNDOSE = "Total number of doses administered",
    TDURD = "Overall duration (days)",
    TNDOSMIS = "Total number of missed doses during study",
    TDURDC = "Overall duration (days)"
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  analyze_vars(
    vars = c("TDURD", "TDURDC", "TDOSE", "TNDOSE"),
    var_labels = var_labels(anl)[c("TDURD", "TDURDC", "TDOSE", "TNDOSE")]
  )

result <- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

result
                                          A: Drug X        B: Placebo      C: Combination 
Parameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————
Drug A                                                                                    
  Overall duration (days)                                                                 
    n                                        75                67                75       
    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  
    Median                                  77.0              80.0              78.0      
    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  
  Overall duration (days)                                                                 
    n                                        75                67                75       
    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    
    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   
    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    
    >= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   
  Total dose administered                                                                 
    n                                        75                67                75       
    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)
    Median                                 6720.0            6480.0            7200.0     
    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0
  Total number of doses administered                                                      
    n                                        75                67                75       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
Drug B                                                                                    
  Overall duration (days)                                                                 
    n                                        59                67                57       
    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  
    Median                                  79.0              76.0              65.0      
    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  
  Overall duration (days)                                                                 
    n                                        59                67                57       
    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   
    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   
    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   
    >= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   
  Total dose administered                                                                 
    n                                        59                67                57       
    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)
    Median                                 6720.0            6240.0            6480.0     
    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0
  Total number of doses administered                                                      
    n                                        59                67                57       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
Experimental use!

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Code
# When summary table contains both categorical and numeric parameters,
# developer needs to do pre-processing to transform dataset to wide format.
adex_avalc_wide <- adex %>%
  filter(PARAMCD == "TDURD") %>%
  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, PARCAT2),
    names_from = PARAMCD,
    values_from = AVALC
  ) %>%
  mutate(
    TDURDC = factor(TDURD, levels = c("0 - 30", "31 - 60", "61 - 90", ">= 91"))
  ) %>%
  select(-TDURD)

anl <- adex %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),
    names_from = PARAMCD,
    values_from = AVAL
  ) %>%
  left_join(adex_avalc_wide, by = c("STUDYID", "USUBJID", "PARCAT2")) %>%
  var_relabel(
    TDOSE = "Total dose administered",
    TNDOSE = "Total number of doses administered",
    TDURD = "Overall duration (days)",
    TNDOSMIS = "Total number of missed doses during study",
    TDURDC = "Overall duration (days)"
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  analyze_vars(
    vars = c("TDURD", "TDURDC", "TDOSE", "TNDOSE"),
    var_labels = var_labels(anl)[c("TDURD", "TDURDC", "TDOSE", "TNDOSE")]
  ) %>%
  count_missed_doses(
    "TNDOSMIS",
    thresholds = c(1, 5, 10, 15),
    var_labels = "Missed Doses"
  )

result <- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

result
                                          A: Drug X        B: Placebo      C: Combination 
Parameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————
Drug A                                                                                    
  Overall duration (days)                                                                 
    n                                        75                67                75       
    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  
    Median                                  77.0              80.0              78.0      
    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  
  Overall duration (days)                                                                 
    n                                        75                67                75       
    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    
    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   
    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    
    >= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   
  Total dose administered                                                                 
    n                                        75                67                75       
    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)
    Median                                 6720.0            6480.0            7200.0     
    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0
  Total number of doses administered                                                      
    n                                        75                67                75       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Missed Doses                                                                            
    n                                        75                67                75       
    At least 1 missed dose               74 (55.2%)         63 (47%)         73 (55.3%)   
    At least 5 missed doses               59 (44%)         49 (36.6%)        59 (44.7%)   
    At least 10 missed doses             41 (30.6%)        38 (28.4%)         37 (28%)    
    At least 15 missed doses             26 (19.4%)        21 (15.7%)        16 (12.1%)   
Drug B                                                                                    
  Overall duration (days)                                                                 
    n                                        59                67                57       
    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  
    Median                                  79.0              76.0              65.0      
    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  
  Overall duration (days)                                                                 
    n                                        59                67                57       
    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   
    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   
    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   
    >= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   
  Total dose administered                                                                 
    n                                        59                67                57       
    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)
    Median                                 6720.0            6240.0            6480.0     
    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0
  Total number of doses administered                                                      
    n                                        59                67                57       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Missed Doses                                                                            
    n                                        59                67                57       
    At least 1 missed dose               56 (41.8%)         63 (47%)         57 (43.2%)   
    At least 5 missed doses              41 (30.6%)        50 (37.3%)        47 (35.6%)   
    At least 10 missed doses             27 (20.1%)        36 (26.9%)        31 (23.5%)   
    At least 15 missed doses             17 (12.7%)        18 (13.4%)        20 (15.2%)   
Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Code
library(tern)
library(dplyr)
library(tidyr)

adsl <- random.cdisc.data::cadsl
adex <- random.cdisc.data::cadex

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adex <- df_explicit_na(adex)

adex <- adex %>%
  filter(PARCAT1 == "OVERALL") %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, PARAM, AVAL, PARCAT2) %>%
  mutate(
    PARAMCD = as.character(PARAMCD),
    AVALC = ""
  ) %>%
  droplevels()

# Add new param tdurd for treatment duration.
set.seed(99)
tdurd_adsl <- adsl %>%
  select(STUDYID, USUBJID, ACTARM) %>%
  mutate(
    PARAMCD = "TDURD",
    PARAM = "Overall duration (days)",
    AVAL = sample(1:150, size = nrow(adsl), replace = TRUE),
    AVALC = case_when(
      0 <= AVAL & AVAL <= 30 ~ "0 - 30",
      31 <= AVAL & AVAL <= 60 ~ "31 - 60",
      61 <= AVAL & AVAL <= 90 ~ "61 - 90",
      TRUE ~ ">= 91"
    )
  )
tdurd <- adex %>%
  filter(PARAMCD == "TNDOSE") %>%
  select(STUDYID, USUBJID, PARCAT2) %>%
  left_join(tdurd_adsl, by = c("STUDYID", "USUBJID"))

# Add new param tndosmis for missed doses.
tndosmis_adsl <- adsl %>%
  select(STUDYID, USUBJID, ACTARM) %>%
  mutate(
    PARAMCD = "TNDOSMIS",
    PARAM = "Total number of missed doses during study",
    AVAL = sample(0:20, size = nrow(adsl), replace = TRUE),
    AVALC = ""
  )
tndosmis <- adex %>%
  filter(PARAMCD == "TNDOSE") %>%
  select(STUDYID, USUBJID, PARCAT2) %>%
  left_join(tndosmis_adsl, by = c("STUDYID", "USUBJID"))

adex <- dplyr::bind_rows(adex, tdurd, tndosmis) %>%
  mutate(PARAM = factor(
    PARAM,
    levels = c(
      "Overall duration (days)", "Total dose administered", "Total number of doses administered",
      "Total number of missed doses during study"
    )
  ))

teal App

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Code
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADEX <- random.cdisc.data::cadex
  adex_labels <- unname(col_labels(ADEX))
  ADEX <- ADEX %>%
    filter(PARCAT1 == "OVERALL") %>%
    mutate(
      AVALCAT1 = case_when(
        PARAMCD == "TDOSE" & AVAL < 5000 ~ "LOW",
        PARAMCD == "TDOSE" & AVAL >= 5000 ~ "HIGH",
        PARAMCD == "TNDOSE" & AVAL < 10 ~ "< 10",
        PARAMCD == "TNDOSE" & AVAL >= 10 ~ ">= 10"
      )
    )
  col_labels(ADEX) <- c(adex_labels, "")
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
Warning: `datanames<-()` was deprecated in teal.data 0.7.0.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data
Code
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADEX <- data[["ADEX"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Exposure Table",
      dataname = "ADEX",
      arm_var = choices_selected(
        choices = variable_choices(ADEX, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEX, c("PARCAT2", "PARAM")),
        selected = c("PARCAT2", "PARAM")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEX, c("AVAL", "AVALCAT1")),
        selected = c("AVAL", "AVALCAT1")
      ),
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = c("TDOSE", "TNDOSE")
      ),
      denominator = choices_selected(
        choices = c("n", "N", "omit"),
        selected = "N"
      )
    )
  ), # Set initial filter state as single study drug to produce smaller table
  filter = teal_slices(
    teal_slice("ADEX", "PARCAT2", selected = "Drug A"),
    teal_slice("ADEX", "AVAL", selected = NULL)
  )
)

shinyApp(app$ui, app$server)

Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://insightsengineering.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADEX <- random.cdisc.data::cadex
  adex_labels <- unname(col_labels(ADEX))
  ADEX <- ADEX %>%
    filter(PARCAT1 == "OVERALL") %>%
    mutate(
      AVALCAT1 = case_when(
        PARAMCD == "TDOSE" & AVAL < 5000 ~ "LOW",
        PARAMCD == "TDOSE" & AVAL >= 5000 ~ "HIGH",
        PARAMCD == "TNDOSE" & AVAL < 10 ~ "< 10",
        PARAMCD == "TNDOSE" & AVAL >= 10 ~ ">= 10"
      )
    )
  col_labels(ADEX) <- c(adex_labels, "")
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADEX <- data[["ADEX"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Exposure Table",
      dataname = "ADEX",
      arm_var = choices_selected(
        choices = variable_choices(ADEX, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEX, c("PARCAT2", "PARAM")),
        selected = c("PARCAT2", "PARAM")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEX, c("AVAL", "AVALCAT1")),
        selected = c("AVAL", "AVALCAT1")
      ),
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = c("TDOSE", "TNDOSE")
      ),
      denominator = choices_selected(
        choices = c("n", "N", "omit"),
        selected = "N"
      )
    )
  ), # Set initial filter state as single study drug to produce smaller table
  filter = teal_slices(
    teal_slice("ADEX", "PARCAT2", selected = "Drug A"),
    teal_slice("ADEX", "AVAL", selected = NULL)
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2025-07-09 17:38:20 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-07-09
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.32 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
 backports               1.5.0    2024-05-23 [1] RSPM
 brio                    1.1.5    2024-04-24 [1] RSPM
 broom                   1.0.8    2025-03-28 [1] RSPM
 bslib                   0.9.0    2025-01-30 [1] RSPM
 cachem                  1.1.0    2024-05-16 [1] RSPM
 callr                   3.7.6    2024-03-25 [1] RSPM
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 chromote                0.5.1    2025-04-24 [1] RSPM
 cli                     3.6.5    2025-04-23 [1] RSPM
 coda                    0.19-4.1 2024-01-31 [1] CRAN (R 4.5.0)
 codetools               0.2-20   2024-03-31 [2] CRAN (R 4.5.0)
 curl                    6.4.0    2025-06-22 [1] RSPM
 data.table              1.17.6   2025-06-17 [1] RSPM
 dichromat               2.0-0.1  2022-05-02 [1] CRAN (R 4.5.0)
 digest                  0.6.37   2024-08-19 [1] RSPM
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 emmeans                 1.11.1   2025-05-04 [1] RSPM
 estimability            1.5.1    2024-05-12 [1] RSPM
 evaluate                1.0.4    2025-06-18 [1] RSPM
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 fastmap                 1.2.0    2024-05-15 [1] RSPM
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 generics                0.1.4    2025-05-09 [1] RSPM
 ggplot2                 3.5.2    2025-04-09 [1] RSPM
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 httr                    1.4.7    2023-08-15 [1] RSPM
 jquerylib               0.1.4    2021-04-26 [1] RSPM
 jsonlite                2.0.0    2025-03-27 [1] RSPM
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 lattice                 0.22-7   2025-04-02 [2] CRAN (R 4.5.0)
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 lifecycle               1.0.4    2023-11-07 [1] RSPM
 logger                  0.4.0    2024-10-22 [1] RSPM
 magrittr              * 2.0.3    2022-03-30 [1] RSPM
 MASS                    7.3-65   2025-02-28 [2] CRAN (R 4.5.0)
 Matrix                  1.7-3    2025-03-11 [1] CRAN (R 4.5.0)
 memoise                 2.0.1    2021-11-26 [1] RSPM
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 mvtnorm                 1.3-3    2025-01-10 [1] RSPM
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 pkgcache                2.2.4    2025-05-26 [1] RSPM
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 promises                1.3.3    2025-05-29 [1] RSPM
 ps                      1.9.1    2025-04-12 [1] RSPM
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 R6                      2.6.1    2025-02-15 [1] RSPM
 random.cdisc.data       0.3.16   2024-10-10 [1] RSPM
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 shiny                 * 1.11.1   2025-07-03 [1] RSPM
 shinycssloaders         1.1.0    2024-07-30 [1] RSPM
 shinyjs                 2.1.0    2021-12-23 [1] RSPM
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 stringi                 1.8.7    2025-03-27 [1] RSPM
 stringr                 1.5.1    2023-11-14 [1] RSPM
 survival                3.8-3    2024-12-17 [2] CRAN (R 4.5.0)
 teal                  * 0.16.0   2025-02-23 [1] RSPM
 teal.code             * 0.6.1    2025-02-14 [1] RSPM
 teal.data             * 0.7.0    2025-01-28 [1] RSPM
 teal.logger             0.4.0    2025-07-08 [1] RSPM
 teal.modules.clinical * 0.10.0   2025-02-28 [1] RSPM
 teal.reporter           0.4.0    2025-01-24 [1] RSPM
 teal.slice            * 0.6.0    2025-02-03 [1] RSPM
 teal.transform        * 0.6.0    2025-02-12 [1] RSPM
 teal.widgets            0.4.3    2025-01-31 [1] RSPM
 tern                  * 0.9.9    2025-06-20 [1] RSPM
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.5
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

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TTET01
LBT01
Source Code
---
title: EXT01
subtitle: Study Drug Exposure Table
---

------------------------------------------------------------------------

{{< include ../../_utils/envir_hook.qmd >}}

```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(tern)
library(dplyr)
library(tidyr)

adsl <- random.cdisc.data::cadsl
adex <- random.cdisc.data::cadex

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adex <- df_explicit_na(adex)

adex <- adex %>%
  filter(PARCAT1 == "OVERALL") %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, PARAM, AVAL, PARCAT2) %>%
  mutate(
    PARAMCD = as.character(PARAMCD),
    AVALC = ""
  ) %>%
  droplevels()

# Add new param tdurd for treatment duration.
set.seed(99)
tdurd_adsl <- adsl %>%
  select(STUDYID, USUBJID, ACTARM) %>%
  mutate(
    PARAMCD = "TDURD",
    PARAM = "Overall duration (days)",
    AVAL = sample(1:150, size = nrow(adsl), replace = TRUE),
    AVALC = case_when(
      0 <= AVAL & AVAL <= 30 ~ "0 - 30",
      31 <= AVAL & AVAL <= 60 ~ "31 - 60",
      61 <= AVAL & AVAL <= 90 ~ "61 - 90",
      TRUE ~ ">= 91"
    )
  )
tdurd <- adex %>%
  filter(PARAMCD == "TNDOSE") %>%
  select(STUDYID, USUBJID, PARCAT2) %>%
  left_join(tdurd_adsl, by = c("STUDYID", "USUBJID"))

# Add new param tndosmis for missed doses.
tndosmis_adsl <- adsl %>%
  select(STUDYID, USUBJID, ACTARM) %>%
  mutate(
    PARAMCD = "TNDOSMIS",
    PARAM = "Total number of missed doses during study",
    AVAL = sample(0:20, size = nrow(adsl), replace = TRUE),
    AVALC = ""
  )
tndosmis <- adex %>%
  filter(PARAMCD == "TNDOSE") %>%
  select(STUDYID, USUBJID, PARCAT2) %>%
  left_join(tndosmis_adsl, by = c("STUDYID", "USUBJID"))

adex <- dplyr::bind_rows(adex, tdurd, tndosmis) %>%
  mutate(PARAM = factor(
    PARAM,
    levels = c(
      "Overall duration (days)", "Total dose administered", "Total number of doses administered",
      "Total number of missed doses during study"
    )
  ))
```

```{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")}
# When summary table contains only categorical or only numeric parameters

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  split_rows_by("PARAM", split_fun = split_fun) %>%
  analyze_vars(vars = "AVAL")

result <- build_table(lyt = lyt, df = adex, alt_counts_df = adsl)

result
```

```{r include = FALSE}
webr_code_labels <- c("variant1")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Table with Optional Analyses

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant2, test = list(result_v2 = "result")}
# When summary table contains both categorical and numeric parameters,
# developer needs to do pre-processing to transform dataset to wide format.
adex_avalc_wide <- adex %>%
  filter(PARAMCD == "TDURD") %>%
  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, PARCAT2),
    names_from = PARAMCD,
    values_from = AVALC
  ) %>%
  mutate(
    TDURDC = factor(TDURD, levels = c("0 - 30", "31 - 60", "61 - 90", ">= 91"))
  ) %>%
  select(-TDURD)

anl <- adex %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),
    names_from = PARAMCD,
    values_from = AVAL
  ) %>%
  left_join(adex_avalc_wide, by = c("STUDYID", "USUBJID", "PARCAT2")) %>%
  var_relabel(
    TDOSE = "Total dose administered",
    TNDOSE = "Total number of doses administered",
    TDURD = "Overall duration (days)",
    TNDOSMIS = "Total number of missed doses during study",
    TDURDC = "Overall duration (days)"
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  analyze_vars(
    vars = c("TDURD", "TDURDC", "TDOSE", "TNDOSE"),
    var_labels = var_labels(anl)[c("TDURD", "TDURDC", "TDOSE", "TNDOSE")]
  )

result <- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

result
```

```{r include = FALSE}
webr_code_labels <- c("variant2")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Table with User-Specified <br/> Categories for Missed Doses

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant3, test = list(result_v3 = "result")}
# When summary table contains both categorical and numeric parameters,
# developer needs to do pre-processing to transform dataset to wide format.
adex_avalc_wide <- adex %>%
  filter(PARAMCD == "TDURD") %>%
  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, PARCAT2),
    names_from = PARAMCD,
    values_from = AVALC
  ) %>%
  mutate(
    TDURDC = factor(TDURD, levels = c("0 - 30", "31 - 60", "61 - 90", ">= 91"))
  ) %>%
  select(-TDURD)

anl <- adex %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),
    names_from = PARAMCD,
    values_from = AVAL
  ) %>%
  left_join(adex_avalc_wide, by = c("STUDYID", "USUBJID", "PARCAT2")) %>%
  var_relabel(
    TDOSE = "Total dose administered",
    TNDOSE = "Total number of doses administered",
    TDURD = "Overall duration (days)",
    TNDOSMIS = "Total number of missed doses during study",
    TDURDC = "Overall duration (days)"
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  analyze_vars(
    vars = c("TDURD", "TDURDC", "TDOSE", "TNDOSE"),
    var_labels = var_labels(anl)[c("TDURD", "TDURDC", "TDOSE", "TNDOSE")]
  ) %>%
  count_missed_doses(
    "TNDOSMIS",
    thresholds = c(1, 5, 10, 15),
    var_labels = "Missed Doses"
  )

result <- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

result
```

```{r include = FALSE}
webr_code_labels <- c("variant3")
```

{{< 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
  ADEX <- random.cdisc.data::cadex
  adex_labels <- unname(col_labels(ADEX))
  ADEX <- ADEX %>%
    filter(PARCAT1 == "OVERALL") %>%
    mutate(
      AVALCAT1 = case_when(
        PARAMCD == "TDOSE" & AVAL < 5000 ~ "LOW",
        PARAMCD == "TDOSE" & AVAL >= 5000 ~ "HIGH",
        PARAMCD == "TNDOSE" & AVAL < 10 ~ "< 10",
        PARAMCD == "TNDOSE" & AVAL >= 10 ~ ">= 10"
      )
    )
  col_labels(ADEX) <- c(adex_labels, "")
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADEX <- data[["ADEX"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Exposure Table",
      dataname = "ADEX",
      arm_var = choices_selected(
        choices = variable_choices(ADEX, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEX, c("PARCAT2", "PARAM")),
        selected = c("PARCAT2", "PARAM")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEX, c("AVAL", "AVALCAT1")),
        selected = c("AVAL", "AVALCAT1")
      ),
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = c("TDOSE", "TNDOSE")
      ),
      denominator = choices_selected(
        choices = c("n", "N", "omit"),
        selected = "N"
      )
    )
  ), # Set initial filter state as single study drug to produce smaller table
  filter = teal_slices(
    teal_slice("ADEX", "PARCAT2", selected = "Drug A"),
    teal_slice("ADEX", "AVAL", selected = NULL)
  )
)

shinyApp(app$ui, app$server)
```

{{< include ../../_utils/shinylive.qmd >}}
:::

{{< include ../../repro.qmd >}}

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