AET01

Safety Summary

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aesi_vars <- c("FATAL", "SER", "SERWD", "SERDSM", "RELSER", "WD", "DSM", "REL", "RELWD", "RELDSM", "SEV")

# Layout for variables from adsl dataset.
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DTHFL" = "Y"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of deaths")
  ) %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DCSREAS" = "ADVERSE EVENT"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of patients withdrawn from study due to an AE"),
    table_names = "tot_wd"
  )

result_adsl <- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one AE",
      nonunique = "Total number of AEs"
    ),
    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = "xx"),
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = aesi_vars,
    denom = "N_col",
    var_labels = "Total number of patients with at least one",
    show_labels = "visible"
  )

result_adae <- build_table(lyt_adae, df = adae, alt_counts_df = adsl)

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adae[1:2, ],
  result_adsl,
  result_adae[3:nrow(result_adae), ]
)

result

                                                              A: Drug X    B: Placebo   C: Combination
                                                               (N=134)      (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  
Total number of AEs                                              502          480            604      
Total number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  
Total number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   
Total number of patients with at least one                                                            
  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   
  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  
  Serious AE leading to withdrawal from treatment             6 (4.5%)     12 (9.0%)       9 (6.8%)   
  Serious AE leading to dose modification/interruption       36 (26.9%)    40 (29.9%)     47 (35.6%)  
  Related Serious AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  
  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  
  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  
  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  
  Related AE leading to withdrawal from treatment             10 (7.5%)     9 (6.7%)      12 (9.1%)   
  Related AE leading to dose modification/interruption       44 (32.8%)    44 (32.8%)     51 (38.6%)  
  Severe AE (at greatest intensity)                          77 (57.5%)    70 (52.2%)     79 (59.8%)

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aesi_vars <- c("FATAL", "SER", "SERWD", "SERDSM", "RELSER", "WD", "DSM", "REL", "RELWD", "RELDSM", "CTC35")
basket_vars <- c("SMQ01", "SMQ02", "CQ01")

# Layout for variables from adsl dataset.
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DTHFL" = "Y"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of deaths")
  ) %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DCSREAS" = "ADVERSE EVENT"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of patients withdrawn from study due to an AE"),
    table_names = "tot_wd"
  )

result_adsl <- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one AE",
      nonunique = "Total number of AEs"
    ),
    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = "xx"),
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = aesi_vars,
    denom = "N_col",
    var_labels = "Total number of patients with at least one",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = basket_vars,
    table_names = "table_aesi",
    denom = "N_col",
    var_labels = "Total number of patients with at least one",
    show_labels = "visible"
  )

result_adae <- build_table(lyt_adae, df = adae, alt_counts_df = adsl)

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adae[1:2, ],
  result_adsl,
  result_adae[3:nrow(result_adae), ]
)

result

                                                              A: Drug X    B: Placebo   C: Combination
                                                               (N=134)      (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  
Total number of AEs                                              502          480            604      
Total number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  
Total number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   
Total number of patients with at least one                                                            
  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   
  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  
  Serious AE leading to withdrawal from treatment             6 (4.5%)     12 (9.0%)       9 (6.8%)   
  Serious AE leading to dose modification/interruption       36 (26.9%)    40 (29.9%)     47 (35.6%)  
  Related Serious AE                                         64 (47.8%)    52 (38.8%)     64 (48.5%)  
  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  
  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  
  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  
  Related AE leading to withdrawal from treatment             10 (7.5%)     9 (6.7%)      12 (9.1%)   
  Related AE leading to dose modification/interruption       44 (32.8%)    44 (32.8%)     51 (38.6%)  
  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  
Total number of patients with at least one                                                            
  C.1.1.1.3/B.2.2.3.1 AESI (BROAD)                           58 (43.3%)    60 (44.8%)     66 (50.0%)  
  SMQ 02 Reference Name                                           0            0              0       
  D.2.1.5.3/A.1.1.1.1 AESI                                   62 (46.3%)    61 (45.5%)     76 (57.6%)

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aesi_vars <- c("FATAL", "SER", "WD", "REL", "CTC35", "CTC45")
# Layout for variables from adsl dataset.
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DTHFL" = "Y"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of deaths")
  ) %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DCSREAS" = "ADVERSE EVENT"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of patients withdrawn from study due to an AE"),
    table_names = "tot_wd"
  ) %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DCSREAS" = "WITHDRAWAL BY SUBJECT"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of patients withdrawn informed consent"),
    table_names = "tot_dscsreas_wd"
  )
result_adsl <- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one AE",
      nonunique = "Total number of AEs"
    ),
    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = "xx"),
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = aesi_vars,
    denom = "N_col",
    var_labels = "Total number of patients with at least one",
    show_labels = "visible"
  )
result_adae <- build_table(lyt_adae, df = adae, alt_counts_df = adsl)

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adae[1:2, ],
  result_adsl,
  result_adae[3:nrow(result_adae), ]
)

result

                                                              A: Drug X    B: Placebo   C: Combination
                                                               (N=134)      (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  
Total number of AEs                                              502          480            604      
Total number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  
Total number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   
Total number of patients withdrawn informed consent           1 (0.7%)      1 (0.7%)       1 (0.8%)   
Total number of patients with at least one                                                            
  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   
  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  
  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  
  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  
  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  
  Grade 4/5 AE                                               77 (57.5%)    70 (52.2%)     79 (59.8%)

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count_subj_vars <- c("FATAL", "SER", "WD", "DSM", "REL", "CTC35")
count_term_vars <- c("SER", "DSM", "REL", "CTC35", "CTC45")
count_ae_vars <- c("SER", "DSM", "REL", "CTC35", "CTC45")

# Layout for variables from adsl dataset.
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DTHFL" = "Y"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of deaths")
  ) %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("DCSREAS" = "ADVERSE EVENT"),
    denom = "N_col",
    .labels = c(count_fraction = "Total number of patients withdrawn from study due to an AE"),
    table_names = "tot_wd"
  )

result_adsl <- build_table(lyt_adsl, df = adsl, alt_counts_df = adsl)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one AE",
      nonunique = "Total number of AEs"
    ),
    .formats = list(unique = format_count_fraction_fixed_dp, nonunique = "xx"),
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = count_subj_vars,
    denom = "N_col",
    var_labels = "Total number of patients with at least one",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "AEDECOD",
    flag_variables = count_term_vars,
    .stats = "count",
    .formats = c(count = "xx"),
    table_names = "table_term",
    var_labels = "Total number of unique preferred terms which are",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID_AESEQ",
    flag_variables = count_ae_vars,
    .stats = "count",
    .formats = c(count = "xx"),
    table_names = "table_ae",
    var_labels = "Total number of adverse events which are",
    show_labels = "visible"
  )

result_adae <- build_table(lyt_adae, df = adae, alt_counts_df = adsl)

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adae[1:2, ],
  result_adsl,
  result_adae[3:nrow(result_adae), ]
)

result

                                                              A: Drug X    B: Placebo   C: Combination
                                                               (N=134)      (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one AE                100 (74.6%)   98 (73.1%)    103 (78.0%)  
Total number of AEs                                              502          480            604      
Total number of deaths                                       25 (18.7%)    23 (17.2%)     22 (16.7%)  
Total number of patients withdrawn from study due to an AE    3 (2.2%)      6 (4.5%)       5 (3.8%)   
Total number of patients with at least one                                                            
  AE with fatal outcome                                       5 (3.7%)      5 (3.7%)       6 (4.5%)   
  Serious AE                                                 85 (63.4%)    80 (59.7%)     87 (65.9%)  
  AE leading to withdrawal from treatment                    20 (14.9%)    24 (17.9%)     26 (19.7%)  
  AE leading to dose modification/interruption               63 (47.0%)    70 (52.2%)     77 (58.3%)  
  Related AE                                                 86 (64.2%)    85 (63.4%)     92 (69.7%)  
  Grade 3-5 AE                                               90 (67.2%)    83 (61.9%)     93 (70.5%)  
Total number of unique preferred terms which are                                                      
  Serious AE                                                      4            4              4       
  AE leading to dose modification/interruption                   10            10             10      
  Related AE                                                      5            5              5       
  Grade 3-5 AE                                                    5            5              5       
  Grade 4/5 AE                                                    3            3              3       
Total number of adverse events which are                                                              
  Serious AE                                                     204          194            245      
  AE leading to dose modification/interruption                   123          135            158      
  Related AE                                                     231          231            290      
  Grade 3-5 AE                                                   249          229            277      
  Grade 4/5 AE                                                   143          134            168

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.

To illustrate, additional variables such as flags (TRUE/FALSE) for select AEs of interest and select AE baskets are added to the adae dataset.

Code

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adae <- random.cdisc.data::cadae

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adae <- df_explicit_na(
  adae,
  omit_columns = c("SMQ01NAM", "SMQ01SC", "SMQ02NAM", "SMQ02SC", "CQ01NAM", "STUDYID", "USUBJID")
)

set.seed(99)

adae <- adae %>%
  mutate(
    AEDECOD = with_label(as.character(AEDECOD), "Dictionary-Derived Term"),
    AESDTH = with_label(
      sample(c("N", "Y"), size = nrow(adae), replace = TRUE, prob = c(0.99, 0.01)),
      "Results in Death"
    ),
    AEACN = with_label(
      sample(
        c("DOSE NOT CHANGED", "DOSE INCREASED", "DRUG INTERRUPTED", "DRUG WITHDRAWN"),
        size = nrow(adae),
        replace = TRUE, prob = c(0.68, 0.02, 0.25, 0.05)
      ),
      "Action Taken with Study Treatment"
    ),
    FATAL = with_label(AESDTH == "Y", "AE with fatal outcome"),
    SEV = with_label(AESEV == "SEVERE", "Severe AE (at greatest intensity)"),
    SER = with_label(AESER == "Y", "Serious AE"),
    SERWD = with_label(AESER == "Y" & AEACN == "DRUG WITHDRAWN", "Serious AE leading to withdrawal from treatment"),
    SERDSM = with_label(
      AESER == "Y" & AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
      "Serious AE leading to dose modification/interruption"
    ),
    RELSER = with_label(AESER == "Y" & AEREL == "Y", "Related Serious AE"),
    WD = with_label(AEACN == "DRUG WITHDRAWN", "AE leading to withdrawal from treatment"),
    DSM = with_label(
      AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"), "AE leading to dose modification/interruption"
    ),
    REL = with_label(AEREL == "Y", "Related AE"),
    RELWD = with_label(AEREL == "Y" & AEACN == "DRUG WITHDRAWN", "Related AE leading to withdrawal from treatment"),
    RELDSM = with_label(
      AEREL == "Y" & AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
      "Related AE leading to dose modification/interruption"
    ),
    CTC35 = with_label(AETOXGR %in% c("3", "4", "5"), "Grade 3-5 AE"),
    CTC45 = with_label(AETOXGR %in% c("4", "5"), "Grade 4/5 AE"),
    SMQ01 = with_label(SMQ01NAM != "", aesi_label(adae$SMQ01NAM, adae$SMQ01SC)),
    SMQ02 = with_label(SMQ02NAM != "", aesi_label(adae$SMQ02NAM, adae$SMQ02SC)),
    CQ01 = with_label(CQ01NAM != "", aesi_label(adae$CQ01NAM)),
    USUBJID_AESEQ = paste(USUBJID, AESEQ, sep = "@@") # Create unique ID per AE in dataset.
  ) %>%
  filter(ANL01FL == "Y")

Code

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adae <- random.cdisc.data::cadae

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adae <- df_explicit_na(
  adae,
  omit_columns = c("SMQ01NAM", "SMQ01SC", "SMQ02NAM", "SMQ02SC", "CQ01NAM", "STUDYID", "USUBJID")
)

set.seed(99)

adae <- adae %>%
  mutate(
    AEDECOD = with_label(as.character(AEDECOD), "Dictionary-Derived Term"),
    AESDTH = with_label(
      sample(c("N", "Y"), size = nrow(adae), replace = TRUE, prob = c(0.99, 0.01)),
      "Results in Death"
    ),
    AEACN = with_label(
      sample(
        c("DOSE NOT CHANGED", "DOSE INCREASED", "DRUG INTERRUPTED", "DRUG WITHDRAWN"),
        size = nrow(adae),
        replace = TRUE, prob = c(0.68, 0.02, 0.25, 0.05)
      ),
      "Action Taken with Study Treatment"
    ),
    FATAL = with_label(AESDTH == "Y", "AE with fatal outcome"),
    SEV = with_label(AESEV == "SEVERE", "Severe AE (at greatest intensity)"),
    SER = with_label(AESER == "Y", "Serious AE"),
    SERWD = with_label(AESER == "Y" & AEACN == "DRUG WITHDRAWN", "Serious AE leading to withdrawal from treatment"),
    SERDSM = with_label(
      AESER == "Y" & AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
      "Serious AE leading to dose modification/interruption"
    ),
    RELSER = with_label(AESER == "Y" & AEREL == "Y", "Related Serious AE"),
    WD = with_label(AEACN == "DRUG WITHDRAWN", "AE leading to withdrawal from treatment"),
    DSM = with_label(
      AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"), "AE leading to dose modification/interruption"
    ),
    REL = with_label(AEREL == "Y", "Related AE"),
    RELWD = with_label(AEREL == "Y" & AEACN == "DRUG WITHDRAWN", "Related AE leading to withdrawal from treatment"),
    RELDSM = with_label(
      AEREL == "Y" & AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
      "Related AE leading to dose modification/interruption"
    ),
    CTC35 = with_label(AETOXGR %in% c("3", "4", "5"), "Grade 3-5 AE"),
    CTC45 = with_label(AETOXGR %in% c("4", "5"), "Grade 4/5 AE"),
    SMQ01 = with_label(SMQ01NAM != "", aesi_label(adae$SMQ01NAM, adae$SMQ01SC)),
    SMQ02 = with_label(SMQ02NAM != "", aesi_label(adae$SMQ02NAM, adae$SMQ02SC)),
    CQ01 = with_label(CQ01NAM != "", aesi_label(adae$CQ01NAM)),
    USUBJID_AESEQ = paste(USUBJID, AESEQ, sep = "@@") # Create unique ID per AE in dataset.
  ) %>%
  filter(ANL01FL == "Y")

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADAE <- random.cdisc.data::cadae

  add_event_flags <- function(dat) {
    dat %>%
      mutate(
        TMPFL_SER = AESER == "Y",
        TMPFL_REL = AEREL == "Y",
        TMPFL_GR5 = AETOXGR == "5",
        TMP_SMQ01 = !is.na(SMQ01NAM),
        TMP_SMQ02 = !is.na(SMQ02NAM),
        TMP_CQ01 = !is.na(CQ01NAM)
      ) %>%
      col_relabel(
        TMPFL_SER = "Serious AE",
        TMPFL_REL = "Related AE",
        TMPFL_GR5 = "Grade 5 AE",
        TMP_SMQ01 = aesi_label(dat[["SMQ01NAM"]], dat[["SMQ01SC"]]),
        TMP_SMQ02 = aesi_label(dat[["SMQ02NAM"]], dat[["SMQ02SC"]]),
        TMP_CQ01 = aesi_label(dat[["CQ01NAM"]])
      )
  }

  # Generating user-defined event flags.
  ADAE <- ADAE %>% add_event_flags()
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADAE <- data[["ADAE"]]
ae_anl_vars <- names(ADAE)[startsWith(names(ADAE), "TMPFL_")]
aesi_vars <- names(ADAE)[startsWith(names(ADAE), "TMP_")]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events_summary(
      label = "Adverse Events Summary",
      dataname = "ADAE",
      arm_var = choices_selected(
        choices = variable_choices("ADSL", c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      flag_var_anl = choices_selected(
        choices = variable_choices("ADAE", ae_anl_vars),
        selected = ae_anl_vars[1],
        keep_order = TRUE,
        fixed = FALSE
      ),
      flag_var_aesi = choices_selected(
        choices = variable_choices("ADAE", aesi_vars),
        selected = aesi_vars[1],
        keep_order = TRUE,
        fixed = FALSE
      ),
      add_total = TRUE
    )
  )
)

Warning in rlang::hash(list(data = data, modules = modules)):
'package:teal.modules.clinical' may not be available when loading

Code

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.

#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://pharmaverse.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
  ADAE <- random.cdisc.data::cadae

  add_event_flags <- function(dat) {
    dat %>%
      mutate(
        TMPFL_SER = AESER == "Y",
        TMPFL_REL = AEREL == "Y",
        TMPFL_GR5 = AETOXGR == "5",
        TMP_SMQ01 = !is.na(SMQ01NAM),
        TMP_SMQ02 = !is.na(SMQ02NAM),
        TMP_CQ01 = !is.na(CQ01NAM)
      ) %>%
      col_relabel(
        TMPFL_SER = "Serious AE",
        TMPFL_REL = "Related AE",
        TMPFL_GR5 = "Grade 5 AE",
        TMP_SMQ01 = aesi_label(dat[["SMQ01NAM"]], dat[["SMQ01SC"]]),
        TMP_SMQ02 = aesi_label(dat[["SMQ02NAM"]], dat[["SMQ02SC"]]),
        TMP_CQ01 = aesi_label(dat[["CQ01NAM"]])
      )
  }

  # Generating user-defined event flags.
  ADAE <- ADAE %>% add_event_flags()
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADAE <- data[["ADAE"]]
ae_anl_vars <- names(ADAE)[startsWith(names(ADAE), "TMPFL_")]
aesi_vars <- names(ADAE)[startsWith(names(ADAE), "TMP_")]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events_summary(
      label = "Adverse Events Summary",
      dataname = "ADAE",
      arm_var = choices_selected(
        choices = variable_choices("ADSL", c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      flag_var_anl = choices_selected(
        choices = variable_choices("ADAE", ae_anl_vars),
        selected = ae_anl_vars[1],
        keep_order = TRUE,
        fixed = FALSE
      ),
      flag_var_aesi = choices_selected(
        choices = variable_choices("ADAE", aesi_vars),
        selected = aesi_vars[1],
        keep_order = TRUE,
        fixed = FALSE
      ),
      add_total = TRUE
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:13:22 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
 backports               1.5.0       2024-05-23 [1] CRAN (R 4.4.1)
 brio                    1.1.5       2024-04-24 [1] CRAN (R 4.4.1)
 broom                   1.0.7       2024-09-26 [1] CRAN (R 4.4.1)
 bslib                   0.8.0       2024-07-29 [1] CRAN (R 4.4.1)
 cachem                  1.1.0       2024-05-16 [1] CRAN (R 4.4.1)
 callr                   3.7.6       2024-03-25 [1] CRAN (R 4.4.1)
 checkmate               2.3.2       2024-07-29 [1] CRAN (R 4.4.1)
 chromote                0.3.1       2024-08-30 [1] CRAN (R 4.4.1)
 cli                     3.6.3       2024-06-21 [1] CRAN (R 4.4.1)
 coda                    0.19-4.1    2024-01-31 [1] CRAN (R 4.4.1)
 codetools               0.2-20      2024-03-31 [2] CRAN (R 4.4.1)
 colorspace              2.1-1       2024-07-26 [1] CRAN (R 4.4.1)
 digest                  0.6.37      2024-08-19 [1] CRAN (R 4.4.1)
 dplyr                 * 1.1.4       2023-11-17 [1] CRAN (R 4.4.1)
 emmeans                 1.10.5      2024-10-14 [1] CRAN (R 4.4.1)
 estimability            1.5.1       2024-05-12 [1] CRAN (R 4.4.1)
 evaluate                1.0.1       2024-10-10 [1] CRAN (R 4.4.1)
 fansi                   1.0.6       2023-12-08 [1] CRAN (R 4.4.1)
 fastmap                 1.2.0       2024-05-15 [1] CRAN (R 4.4.1)
 fontawesome             0.5.2       2023-08-19 [1] CRAN (R 4.4.1)
 forcats                 1.0.0       2023-01-29 [1] CRAN (R 4.4.1)
 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9.9003  2024-11-02 [1] git2r (https://github.com/insightsengineering/formatters.git@84e8c3e)
 geepack                 1.3.12      2024-09-23 [1] CRAN (R 4.4.1)
 generics                0.1.3       2022-07-05 [1] CRAN (R 4.4.1)
 ggplot2                 3.5.1       2024-04-23 [1] CRAN (R 4.4.1)
 glue                    1.8.0       2024-09-30 [1] CRAN (R 4.4.1)
 gtable                  0.3.5       2024-04-22 [1] CRAN (R 4.4.1)
 htmltools               0.5.8.1     2024-04-04 [1] CRAN (R 4.4.1)
 htmlwidgets             1.6.4       2023-12-06 [1] CRAN (R 4.4.1)
 httpuv                  1.6.15      2024-03-26 [1] CRAN (R 4.4.1)
 jquerylib               0.1.4       2021-04-26 [1] CRAN (R 4.4.1)
 jsonlite                1.8.9       2024-09-20 [1] CRAN (R 4.4.1)
 knitr                   1.48        2024-07-07 [1] CRAN (R 4.4.1)
 later                   1.3.2       2023-12-06 [1] CRAN (R 4.4.1)
 lattice                 0.22-6      2024-03-20 [2] CRAN (R 4.4.1)
 lifecycle               1.0.4       2023-11-07 [1] CRAN (R 4.4.1)
 logger                  0.3.0       2024-03-05 [1] CRAN (R 4.4.1)
 magrittr              * 2.0.3       2022-03-30 [1] CRAN (R 4.4.1)
 MASS                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0       2024-04-26 [1] CRAN (R 4.4.1)
 memoise                 2.0.1       2021-11-26 [1] CRAN (R 4.4.1)
 mime                    0.12        2021-09-28 [1] CRAN (R 4.4.1)
 multcomp                1.4-26      2024-07-18 [1] CRAN (R 4.4.1)
 munsell                 0.5.1       2024-04-01 [1] CRAN (R 4.4.1)
 mvtnorm                 1.3-1       2024-09-03 [1] CRAN (R 4.4.1)
 nestcolor               0.1.2.9017  2024-11-02 [1] git2r (https://github.com/insightsengineering/nestcolor.git@3d2ce62)
 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0       2023-03-22 [1] CRAN (R 4.4.1)
 pkgconfig               2.0.3       2019-09-22 [1] CRAN (R 4.4.1)
 processx                3.8.4       2024-03-16 [1] CRAN (R 4.4.1)
 promises                1.3.0       2024-04-05 [1] CRAN (R 4.4.1)
 ps                      1.8.0       2024-09-12 [1] CRAN (R 4.4.1)
 purrr                   1.0.2       2023-08-10 [1] CRAN (R 4.4.1)
 R6                      2.5.1       2021-08-19 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16.9001 2024-11-02 [1] git2r (https://github.com/insightsengineering/random.cdisc.data.git@a65bfbe)
 rbibutils               2.3         2024-10-04 [1] CRAN (R 4.4.1)
 Rcpp                    1.0.13      2024-07-17 [1] CRAN (R 4.4.1)
 Rdpack                  2.6.1       2024-08-06 [1] CRAN (R 4.4.1)
 rlang                   1.1.4       2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28        2024-08-17 [1] CRAN (R 4.4.1)
 rtables               * 0.6.10.9002 2024-11-02 [1] git2r (https://github.com/insightsengineering/rtables.git@d2cc7a0)
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9       2024-03-15 [1] CRAN (R 4.4.1)
 scales                  1.3.0       2023-11-28 [1] CRAN (R 4.4.1)
 sessioninfo             1.2.2       2021-12-06 [1] CRAN (R 4.4.1)
 shiny                 * 1.9.1       2024-08-01 [1] CRAN (R 4.4.1)
 shinycssloaders         1.1.0       2024-07-30 [1] CRAN (R 4.4.1)
 shinyjs                 2.1.0       2021-12-23 [1] CRAN (R 4.4.1)
 shinyvalidate           0.1.3       2023-10-04 [1] CRAN (R 4.4.1)
 shinyWidgets            0.8.7       2024-09-23 [1] CRAN (R 4.4.1)
 stringi                 1.8.4       2024-05-06 [1] CRAN (R 4.4.1)
 stringr                 1.5.1       2023-11-14 [1] CRAN (R 4.4.1)
 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2.9079 2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.git@53c423d)
 teal.code             * 0.5.0.9012  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.code.git@86aa265)
 teal.data             * 0.6.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.data.git@eca9695)
 teal.logger             0.3.0.9000  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.logger.git@cd9cc7e)
 teal.modules.clinical * 0.9.1.9030  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.modules.clinical.git@5cf76ac)
 teal.reporter           0.3.1.9016  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.reporter.git@4fefa7e)
 teal.slice            * 0.5.1.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.slice.git@25f4101)
 teal.transform        * 0.5.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.transform.git@0b855e3)
 teal.widgets            0.4.2.9022  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.widgets.git@8a39dd4)
 tern                  * 0.9.6.9013  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.git@435fd51)
 tern.gee                0.1.5.9004  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.gee.git@458172d)
 testthat                3.2.1.1     2024-04-14 [1] CRAN (R 4.4.1)
 TH.data                 1.1-2       2023-04-17 [1] CRAN (R 4.4.1)
 tibble                  3.2.1       2023-03-20 [1] CRAN (R 4.4.1)
 tidyr                   1.3.1       2024-01-24 [1] CRAN (R 4.4.1)
 tidyselect              1.2.1       2024-03-11 [1] CRAN (R 4.4.1)
 utf8                    1.2.4       2023-10-22 [1] CRAN (R 4.4.1)
 vctrs                   0.6.5       2023-12-01 [1] CRAN (R 4.4.1)
 webshot                 0.5.5       2023-06-26 [1] CRAN (R 4.4.1)
 webshot2                0.1.1       2023-08-11 [1] CRAN (R 4.4.1)
 websocket               1.4.2       2024-07-22 [1] CRAN (R 4.4.1)
 withr                   3.0.1       2024-07-31 [1] CRAN (R 4.4.1)
 xfun                    0.48        2024-10-03 [1] CRAN (R 4.4.1)
 xtable                  1.8-4       2019-04-21 [1] CRAN (R 4.4.1)
 yaml                    2.3.10      2024-07-26 [1] CRAN (R 4.4.1)
 zoo                     1.8-12      2023-04-13 [1] CRAN (R 4.4.1)

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

`.lock` file

Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

Download

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file