AET02

Adverse Events

---

Standard Table

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  add_overall_col(label = "All Patients") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Overall total number of events"
    )
  ) %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = -1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

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

MedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients
  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) 
Overall total number of events                                   502          480            604             1586    
cl A.1                                                                                                               
  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) 
  Total number of events                                         115           99            137             351     
  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) 
  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) 
cl B.1                                                                                                               
  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) 
  Total number of events                                         44            43             50             137     
  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) 
cl B.2                                                                                                               
  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) 
  Total number of events                                         102          106            127             335     
  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) 
  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) 
cl C.1                                                                                                               
  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) 
  Total number of events                                         47            51             54             152     
  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) 
cl C.2                                                                                                               
  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) 
  Total number of events                                         39            40             57             136     
  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) 
cl D.1                                                                                                               
  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) 
  Total number of events                                         106           84            114             304     
  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) 
  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) 
cl D.2                                                                                                               
  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) 
  Total number of events                                         49            57             65             171     
  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) 

Try this using WebR

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.

The variable result corresponds to the adverse events table. However, it includes many empty rows accounting for events which were not reported. The table can be post-processed to prune empty rows and to sort rows, for example by occurrence.

Preview

result <- result %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

result

MedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients
  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) 
Overall total number of events                                   502          480            604             1586    
cl A.1                                                                                                               
  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) 
  Total number of events                                         115           99            137             351     
  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) 
  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) 
cl B.2                                                                                                               
  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) 
  Total number of events                                         102          106            127             335     
  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) 
  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) 
cl D.1                                                                                                               
  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) 
  Total number of events                                         106           84            114             304     
  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) 
  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) 
cl D.2                                                                                                               
  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) 
  Total number of events                                         49            57             65             171     
  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) 
cl C.2                                                                                                               
  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) 
  Total number of events                                         39            40             57             136     
  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) 
cl B.1                                                                                                               
  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) 
  Total number of events                                         44            43             50             137     
  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) 
cl C.1                                                                                                               
  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) 
  Total number of events                                         47            51             54             152     
  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) 

Try this using WebR

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.

Table with Event Totals

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  add_overall_col(label = "All Patients") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Overall total number of events"
    )
  ) %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event"
    )
  ) %>%
  count_occurrences(vars = "AEDECOD", .indent_mods = -1L) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L) %>%
  count_values(
    "STUDYID",
    values = as.character(unique(adae$STUDYID)),
    .stats = "count",
    .labels = c(count = "Total number of events"),
    .indent_mods = c(count = -1L)
  )

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  ) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  )

result

MedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination   All Patients
  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)         (N=400)   
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)    103 (78.0%)     301 (75.2%) 
Overall total number of events                                   502          480            604             1586    
cl A.1                                                                                                               
  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)     202 (50.5%) 
  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)     128 (32.0%) 
  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)     122 (30.5%) 
  Total number of events                                         115           99            137             351     
cl B.2                                                                                                               
  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)     74 (56.1%)     192 (48.0%) 
  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)     123 (30.8%) 
  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)     119 (29.8%) 
  Total number of events                                         102          106            127             335     
cl D.1                                                                                                               
  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)     68 (51.5%)     186 (46.5%) 
  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)     120 (30.0%) 
  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)     112 (28.0%) 
  Total number of events                                         106           84            114             304     
cl D.2                                                                                                               
  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) 
  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)     133 (33.2%) 
  Total number of events                                         49            57             65             171     
cl C.2                                                                                                               
  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) 
  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)     112 (28.0%) 
  Total number of events                                         39            40             57             136     
cl B.1                                                                                                               
  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) 
  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)     111 (27.8%) 
  Total number of events                                         44            43             50             137     
cl C.1                                                                                                               
  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) 
  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)     106 (26.5%) 
  Total number of events                                         47            51             54             152     

Try this using WebR

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.

Table with High-Level Term

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Overall total number of events"
    )
  ) %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  split_rows_by(
    "AEHLT",
    child_labels = "visible",
    nested = TRUE,
    split_fun = split_fun,
    indent_mod = -1L,
    label_pos = "topleft",
    split_label = obj_label(adae$AEHLT)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(vars = "AEDECOD", .indent_mods = -1L) %>%
  append_varlabels(adae, c("AEDECOD"), indent = 2L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEHLT"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEHLT", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

result

MedDRA System Organ Class                                                                               
  High Level Term                                               A: Drug X    B: Placebo   C: Combination
    MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event       100 (74.6%)   98 (73.1%)    103 (78.0%)  
Overall total number of events                                     502          480            604      
cl A.1                                                                                                  
  Total number of patients with at least one adverse event     68 (50.7%)    58 (43.3%)     76 (57.6%)  
  Total number of events                                           115           99            137      
  hlt A.1.1.1                                                                                           
    Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)     76 (57.6%)  
    Total number of events                                         115           99            137      
    dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)  
    dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)  
cl B.2                                                                                                  
  Total number of patients with at least one adverse event     62 (46.3%)    56 (41.8%)     74 (56.1%)  
  Total number of events                                           102          106            127      
  hlt B.2.2.3                                                                                           
    Total number of patients with at least one adverse event   38 (28.4%)    40 (29.9%)     45 (34.1%)  
    Total number of events                                         50            55             68      
    dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)  
  hlt B.2.1.2                                                                                           
    Total number of patients with at least one adverse event   39 (29.1%)    34 (25.4%)     46 (34.8%)  
    Total number of events                                         52            51             59      
    dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)  
cl D.1                                                                                                  
  Total number of patients with at least one adverse event     64 (47.8%)    54 (40.3%)     68 (51.5%)  
  Total number of events                                           106           84            114      
  hlt D.1.1.1                                                                                           
    Total number of patients with at least one adverse event   42 (31.3%)    32 (23.9%)     46 (34.8%)  
    Total number of events                                         52            40             64      
    dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)  
  hlt D.1.1.4                                                                                           
    Total number of patients with at least one adverse event   38 (28.4%)    34 (25.4%)     40 (30.3%)  
    Total number of events                                         54            44             50      
    dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)  
cl D.2                                                                                                  
  Total number of patients with at least one adverse event     37 (27.6%)    46 (34.3%)     50 (37.9%)  
  Total number of events                                           49            57             65      
  hlt D.2.1.5                                                                                           
    Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)     50 (37.9%)  
    Total number of events                                         49            57             65      
    dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)  
cl C.2                                                                                                  
  Total number of patients with at least one adverse event     28 (20.9%)    36 (26.9%)     48 (36.4%)  
  Total number of events                                           39            40             57      
  hlt C.2.1.2                                                                                           
    Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)     48 (36.4%)  
    Total number of events                                         39            40             57      
    dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)  
cl B.1                                                                                                  
  Total number of patients with at least one adverse event     38 (28.4%)    37 (27.6%)     36 (27.3%)  
  Total number of events                                           44            43             50      
  hlt B.1.1.1                                                                                           
    Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)     36 (27.3%)  
    Total number of events                                         44            43             50      
    dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)  
cl C.1                                                                                                  
  Total number of patients with at least one adverse event     36 (26.9%)    34 (25.4%)     36 (27.3%)  
  Total number of events                                           47            51             54      
  hlt C.1.1.1                                                                                           
    Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)     36 (27.3%)  
    Total number of events                                         47            51             54      
    dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table with Preferred Terms Only

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    ),
    show_labels = "hidden"
  ) %>%
  count_occurrences(vars = "AEDECOD") %>%
  append_varlabels(adae, "AEDECOD")

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  sort_at_path(
    path = c("AEDECOD"),
    scorefun = score_occurrences
  )

result

                                                            A: Drug X    B: Placebo   C: Combination
MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event   100 (74.6%)   98 (73.1%)    103 (78.0%)  
Total number of events                                         502          480            604      
dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)     50 (37.9%)  
dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)     52 (39.4%)  
dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)     45 (34.1%)  
dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)     42 (31.8%)  
dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)     46 (34.8%)  
dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)     46 (34.8%)  
dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)     48 (36.4%)  
dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)     40 (30.3%)  
dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)     36 (27.3%)  
dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table with Fill-In of Treatment Groups

Preview

adae_5 <- adae %>% dplyr::filter(ACTARM != "C: Combination")

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Overall total number of events"
    )
  ) %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(vars = "AEDECOD", .indent_mods = -1L) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae_5, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

result

MedDRA System Organ Class                                     A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term                                        (N=134)      (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     100 (74.6%)   98 (73.1%)         0       
Overall total number of events                                   502          480             0       
cl A.1                                                                                                
  Total number of patients with at least one adverse event   68 (50.7%)    58 (43.3%)         0       
  Total number of events                                         115           99             0       
  dcd A.1.1.1.2                                              41 (30.6%)    39 (29.1%)         0       
  dcd A.1.1.1.1                                              45 (33.6%)    31 (23.1%)         0       
cl B.2                                                                                                
  Total number of patients with at least one adverse event   62 (46.3%)    56 (41.8%)         0       
  Total number of events                                         102          106             0       
  dcd B.2.2.3.1                                              38 (28.4%)    40 (29.9%)         0       
  dcd B.2.1.2.1                                              39 (29.1%)    34 (25.4%)         0       
cl D.1                                                                                                
  Total number of patients with at least one adverse event   64 (47.8%)    54 (40.3%)         0       
  Total number of events                                         106           84             0       
  dcd D.1.1.1.1                                              42 (31.3%)    32 (23.9%)         0       
  dcd D.1.1.4.2                                              38 (28.4%)    34 (25.4%)         0       
cl D.2                                                                                                
  Total number of patients with at least one adverse event   37 (27.6%)    46 (34.3%)         0       
  Total number of events                                         49            57             0       
  dcd D.2.1.5.3                                              37 (27.6%)    46 (34.3%)         0       
cl B.1                                                                                                
  Total number of patients with at least one adverse event   38 (28.4%)    37 (27.6%)         0       
  Total number of events                                         44            43             0       
  dcd B.1.1.1.1                                              38 (28.4%)    37 (27.6%)         0       
cl C.1                                                                                                
  Total number of patients with at least one adverse event   36 (26.9%)    34 (25.4%)         0       
  Total number of events                                         47            51             0       
  dcd C.1.1.1.3                                              36 (26.9%)    34 (25.4%)         0       
cl C.2                                                                                                
  Total number of patients with at least one adverse event   28 (20.9%)    36 (26.9%)         0       
  Total number of events                                         39            40             0       
  dcd C.2.1.2.1                                              28 (20.9%)    36 (26.9%)         0       

Try this using WebR

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.

Table of AEs with an Incidence Rate \(\geq\) 5% in Any Treatment Group (subsetting preferred terms based on frequency)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    indent_mod = -1L,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = 1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition <- has_fraction_in_any_col(
  atleast = 0.05,
  col_names = names(table(adsl$ACTARM))
)
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  
  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  
cl B.2                                                              
  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  
  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  
  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  
cl D.2                                                              
  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  
cl B.1                                                              
  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  
cl C.1                                                              
  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table of AEs with an Incidence Rate \(\geq\) 5% in Any Treatment Group (subsetting preferred terms based on frequency with high-level terms)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = TRUE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  split_rows_by(
    "AEHLT",
    child_labels = "visible",
    nested = TRUE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEHLT)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(vars = "AEDECOD") %>%
  append_varlabels(adae, c("AEDECOD"), indent = 2L)

result <- build_table(lyt, adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols,
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEHLT"),
    scorefun = cont_n_allcols,
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEHLT", "*", "AEDECOD"),
    scorefun = score_occurrences,
    decreasing = TRUE
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition <- has_fraction_in_any_col(
  atleast = 0.05,
  col_names = names(table(adsl$ACTARM))
)
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class                                           
  High Level Term           A: Drug X    B: Placebo   C: Combination
    MedDRA Preferred Term    (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  hlt A.1.1.1                                                       
    dcd A.1.1.1.1           45 (33.6%)   31 (23.1%)     52 (39.4%)  
    dcd A.1.1.1.2           41 (30.6%)   39 (29.1%)     42 (31.8%)  
cl B.2                                                              
  hlt B.2.2.3                                                       
    dcd B.2.2.3.1           38 (28.4%)   40 (29.9%)     45 (34.1%)  
  hlt B.2.1.2                                                       
    dcd B.2.1.2.1           39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  hlt D.1.1.1                                                       
    dcd D.1.1.1.1           42 (31.3%)   32 (23.9%)     46 (34.8%)  
  hlt D.1.1.4                                                       
    dcd D.1.1.4.2           38 (28.4%)   34 (25.4%)     40 (30.3%)  
cl D.2                                                              
  hlt D.2.1.5                                                       
    dcd D.2.1.5.3           37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  hlt C.2.1.2                                                       
    dcd C.2.1.2.1           28 (20.9%)   36 (26.9%)     48 (36.4%)  
cl B.1                                                              
  hlt B.1.1.1                                                       
    dcd B.1.1.1.1           38 (28.4%)   37 (27.6%)     36 (27.3%)  
cl C.1                                                              
  hlt C.1.1.1                                                       
    dcd C.1.1.1.3           36 (26.9%)   34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table of AEs with an Incidence Rate \(\geq\) 10% in Any Treatment Group (subsetting preferred terms based on frequency)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    indent_mod = -1L,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = 1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition <- has_fraction_in_any_col(
  atleast = 0.10,
  col_names = names(table(adsl$ACTARM))
)
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  
  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  
cl B.2                                                              
  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  
  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  
  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  
cl D.2                                                              
  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  
cl B.1                                                              
  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  
cl C.1                                                              
  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table of AEs with an Incidence Rate \(\geq\) 3 Patients in Any Treatment Group (subsetting preferred terms based on number of patients)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    indent_mod = -1L,
    split_fun = split_fun,
    split_label = obj_label(adae$AEBODSYS),
    label_pos = "topleft"
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = 1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition <- has_count_in_any_col(
  atleast = 3,
  col_names = names(table(adsl$ACTARM))
)
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  
  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  
cl B.2                                                              
  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  
  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  
  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  
cl D.2                                                              
  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  
cl B.1                                                              
  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  
cl C.1                                                              
  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table of AEs with a Difference in Incidence Rate \(\geq\) 5% Between Any Treatment (subsetting preferred terms based on difference in percentage between treatment groups)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    indent_mod = -1L,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = 1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition <- has_fractions_difference(
  atleast = 0.05,
  col_names = levels(adsl$ACTARM)
)
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  
cl B.2                                                              
  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  
  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  
cl D.2                                                              
  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  

Try this using WebR

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.

Table of AEs with an Incidence Rate \(\geq\) 5% in B: Placebo (subsetting preferred terms based on frequency for a particular treatment group)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    indent_mod = -1L,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = 1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition <- has_fraction_in_cols(
  atleast = 0.05,
  col_names = c("B: Placebo")
)
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  
  dcd A.1.1.1.2             41 (30.6%)   39 (29.1%)     42 (31.8%)  
cl B.2                                                              
  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  
  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  
  dcd D.1.1.4.2             38 (28.4%)   34 (25.4%)     40 (30.3%)  
cl D.2                                                              
  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  
cl B.1                                                              
  dcd B.1.1.1.1             38 (28.4%)   37 (27.6%)     36 (27.3%)  
cl C.1                                                              
  dcd C.1.1.1.3             36 (26.9%)   34 (25.4%)     36 (27.3%)  

Try this using WebR

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.

Table of AEs with a Difference in Incidence Rate \(\geq\) 5% Between Arm A and Arm B or Arm C (displaying preferred terms with a difference of at least x% between selected treatment groups)

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    indent_mod = -1L,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = 1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = cont_n_allcols
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences
  )

criteria_fun <- function(tr) is(tr, "ContentRow")
result <- trim_rows(result, criteria = criteria_fun)

row_condition1 <- has_fractions_difference(atleast = 0.05, col_names = c("A: Drug X", "B: Placebo"))
row_condition2 <- has_fractions_difference(atleast = 0.05, col_names = c("A: Drug X", "C: Combination"))
row_condition <- row_condition1 | row_condition2
result <- prune_table(result, keep_rows(row_condition))

result

MedDRA System Organ Class   A: Drug X    B: Placebo   C: Combination
  MedDRA Preferred Term      (N=134)      (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————
cl A.1                                                              
  dcd A.1.1.1.1             45 (33.6%)   31 (23.1%)     52 (39.4%)  
cl B.2                                                              
  dcd B.2.2.3.1             38 (28.4%)   40 (29.9%)     45 (34.1%)  
  dcd B.2.1.2.1             39 (29.1%)   34 (25.4%)     46 (34.8%)  
cl D.1                                                              
  dcd D.1.1.1.1             42 (31.3%)   32 (23.9%)     46 (34.8%)  
cl D.2                                                              
  dcd D.2.1.5.3             37 (27.6%)   46 (34.3%)     50 (37.9%)  
cl C.2                                                              
  dcd C.2.1.2.1             28 (20.9%)   36 (26.9%)     48 (36.4%)  

Try this using WebR

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.

Data Setup

library(dplyr)
library(tern)

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) %>%
  var_relabel(
    AEBODSYS = "MedDRA System Organ Class",
    AEDECOD = "MedDRA Preferred Term"
  ) %>%
  filter(ANL01FL == "Y")

# Define the split function
split_fun <- drop_split_levels

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  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)
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Adverse Event Table",
      dataname = "ADAE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
        selected = c("AEDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
        selected = "AEBODSYS"
      ),
      add_total = TRUE,
      event_type = "adverse event"
    )
  )
)

shinyApp(app$ui, app$server)

Try this using shinylive

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, {
  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)
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Adverse Event Table",
      dataname = "ADAE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
        selected = c("AEDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
        selected = "AEBODSYS"
      ),
      add_total = TRUE,
      event_type = "adverse event"
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:13:51 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