MHT01

Medical History

Output

Code
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c("Total number of patients with at least one condition", "Total number of conditions")
  ) %>%
  split_rows_by(
    var = "MHBODSYS",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(admh_f$MHBODSYS)
  ) %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c("Total number of patients with at least one condition", "Total number of conditions"),
    show_labels = "hidden"
  ) %>%
  count_occurrences(vars = "MHDECOD") %>%
  append_varlabels(admh_f, "MHDECOD", indent = 1L)

result <- build_table(lyt, admh_f, alt_counts_df = adsl_f) %>%
  prune_table()

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 condition     122 (91.0%)   123 (91.8%)    120 (90.9%)  
Total number of conditions                                   609           622            703      
cl A                                                                                               
  Total number of patients with at least one condition   78 (58.2%)    75 (56.0%)      89 (67.4%)  
  Total number of conditions                                 132           130            160      
  trm A_1/2                                              50 (37.3%)    45 (33.6%)      63 (47.7%)  
  trm A_2/2                                              48 (35.8%)    48 (35.8%)      50 (37.9%)  
cl B                                                                                               
  Total number of patients with at least one condition   96 (71.6%)    89 (66.4%)      97 (73.5%)  
  Total number of conditions                                 185           198            205      
  trm B_1/3                                              47 (35.1%)    49 (36.6%)      43 (32.6%)  
  trm B_2/3                                              49 (36.6%)    44 (32.8%)      52 (39.4%)  
  trm B_3/3                                              48 (35.8%)    54 (40.3%)      51 (38.6%)  
cl C                                                                                               
  Total number of patients with at least one condition   67 (50.0%)    75 (56.0%)      79 (59.8%)  
  Total number of conditions                                 103           116            129      
  trm C_1/2                                              43 (32.1%)    46 (34.3%)      43 (32.6%)  
  trm C_2/2                                              35 (26.1%)    48 (35.8%)      55 (41.7%)  
cl D                                                                                               
  Total number of patients with at least one condition   96 (71.6%)    90 (67.2%)      98 (74.2%)  
  Total number of conditions                                 189           178            209      
  trm D_1/3                                              50 (37.3%)    42 (31.3%)      51 (38.6%)  
  trm D_2/3                                              48 (35.8%)    42 (31.3%)      50 (37.9%)  
  trm D_3/3                                              47 (35.1%)    58 (43.3%)      57 (43.2%)

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Code
admh_f_prior <- admh_f %>%
  filter(ASTDY <= 0)

result <- build_table(lyt, admh_f_prior, alt_counts_df = adsl_f) %>%
  prune_table()

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 condition         0           0           1 (0.8%)   
Total number of conditions                                   0           0              1       
cl D                                                                                            
  Total number of patients with at least one condition       0           0           1 (0.8%)   
  Total number of conditions                                 0           0              1       
  trm D_2/3                                                  0           0           1 (0.8%)

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Code
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique"),
    .labels = c("Total number of patients with at least one condition")
  ) %>%
  split_rows_by(
    var = "MHBODSYS",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(admh_f$MHBODSYS)
  ) %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique"),
    .labels = c("Total number of patients with at least one condition"),
    show_labels = "hidden"
  ) %>%
  count_occurrences(vars = "MHDECOD") %>%
  append_varlabels(admh_f, "MHDECOD", indent = 1L)

result <- build_table(lyt, admh_f, alt_counts_df = adsl) %>%
  prune_table()

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 condition     122 (91.0%)   123 (91.8%)    120 (90.9%)  
cl A                                                                                               
  Total number of patients with at least one condition   78 (58.2%)    75 (56.0%)      89 (67.4%)  
  trm A_1/2                                              50 (37.3%)    45 (33.6%)      63 (47.7%)  
  trm A_2/2                                              48 (35.8%)    48 (35.8%)      50 (37.9%)  
cl B                                                                                               
  Total number of patients with at least one condition   96 (71.6%)    89 (66.4%)      97 (73.5%)  
  trm B_1/3                                              47 (35.1%)    49 (36.6%)      43 (32.6%)  
  trm B_2/3                                              49 (36.6%)    44 (32.8%)      52 (39.4%)  
  trm B_3/3                                              48 (35.8%)    54 (40.3%)      51 (38.6%)  
cl C                                                                                               
  Total number of patients with at least one condition   67 (50.0%)    75 (56.0%)      79 (59.8%)  
  trm C_1/2                                              43 (32.1%)    46 (34.3%)      43 (32.6%)  
  trm C_2/2                                              35 (26.1%)    48 (35.8%)      55 (41.7%)  
cl D                                                                                               
  Total number of patients with at least one condition   96 (71.6%)    90 (67.2%)      98 (74.2%)  
  trm D_1/3                                              50 (37.3%)    42 (31.3%)      51 (38.6%)  
  trm D_2/3                                              48 (35.8%)    42 (31.3%)      50 (37.9%)  
  trm D_3/3                                              47 (35.1%)    58 (43.3%)      57 (43.2%)

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Conditions are further sorted by decreasing high level terms and low level terms.

Code
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  add_overall_col("All Patients") %>%
  analyze_num_patients(
    "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(unique = "Total number of patients with at least one event", nonunique = "Total number of conditions")
  ) %>%
  split_rows_by(
    var = "MHBODSYS",
    split_fun = split_fun,
    child_labels = "visible",
    label_pos = "topleft",
    split_label = obj_label(admh_f$MHBODSYS)
  ) %>%
  summarize_num_patients(
    "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(unique = "Total number of patients with at least one event", nonunique = "Total number of conditions")
  ) %>%
  count_occurrences(vars = "MHDECOD", .indent_mods = -1L) %>%
  append_varlabels(admh_f, "MHDECOD", indent = 1L)

scorefun_hlt <- cont_n_allcols
scorefun_llt <- score_occurrences_cols(col_indices = nlevels(adsl_f$ACTARM) + 1)

result <- build_table(lyt, admh_f, alt_counts_df = adsl_f) %>%
  prune_table() %>%
  sort_at_path(path = c("MHBODSYS"), scorefun = scorefun_hlt) %>%
  sort_at_path(path = c("MHBODSYS", "*", "MHDECOD"), scorefun = scorefun_llt)

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 event     122 (91.0%)   123 (91.8%)    120 (90.9%)     365 (91.2%) 
Total number of conditions                               609           622            703             1934    
cl D                                                                                                          
  Total number of patients with at least one event   96 (71.6%)    90 (67.2%)      98 (74.2%)     284 (71.0%) 
  Total number of conditions                             189           178            209             576     
  trm D_3/3                                          47 (35.1%)    58 (43.3%)      57 (43.2%)     162 (40.5%) 
  trm D_1/3                                          50 (37.3%)    42 (31.3%)      51 (38.6%)     143 (35.8%) 
  trm D_2/3                                          48 (35.8%)    42 (31.3%)      50 (37.9%)     140 (35.0%) 
cl B                                                                                                          
  Total number of patients with at least one event   96 (71.6%)    89 (66.4%)      97 (73.5%)     282 (70.5%) 
  Total number of conditions                             185           198            205             588     
  trm B_3/3                                          48 (35.8%)    54 (40.3%)      51 (38.6%)     153 (38.2%) 
  trm B_2/3                                          49 (36.6%)    44 (32.8%)      52 (39.4%)     145 (36.2%) 
  trm B_1/3                                          47 (35.1%)    49 (36.6%)      43 (32.6%)     139 (34.8%) 
cl A                                                                                                          
  Total number of patients with at least one event   78 (58.2%)    75 (56.0%)      89 (67.4%)     242 (60.5%) 
  Total number of conditions                             132           130            160             422     
  trm A_1/2                                          50 (37.3%)    45 (33.6%)      63 (47.7%)     158 (39.5%) 
  trm A_2/2                                          48 (35.8%)    48 (35.8%)      50 (37.9%)     146 (36.5%) 
cl C                                                                                                          
  Total number of patients with at least one event   67 (50.0%)    75 (56.0%)      79 (59.8%)     221 (55.2%) 
  Total number of conditions                             103           116            129             348     
  trm C_2/2                                          35 (26.1%)    48 (35.8%)      55 (41.7%)     138 (34.5%) 
  trm C_1/2                                          43 (32.1%)    46 (34.3%)      43 (32.6%)     132 (33.0%)

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Code
library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
admh <- random.cdisc.data::cadmh

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

adsl_f <- adsl %>%
  filter(SAFFL == "Y") %>%
  select(USUBJID, ACTARM)

admh_f <- admh %>%
  filter(SAFFL == "Y" & MHBODSYS != "" & MHDECOD != "") %>%
  var_relabel(
    MHBODSYS = "MedDRA System Organ Class",
    MHDECOD = "MedDRA Preferred Term"
  )

teal App

Code
library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl %>%
    filter(SAFFL == "Y")
  ADMH <- random.cdisc.data::cadmh %>%
    filter(SAFFL == "Y" & MHBODSYS != "" & MHDECOD != "")
})
datanames <- c("ADSL", "ADMH")
datanames(data) <- datanames
Warning: `datanames<-()` was deprecated in teal.data 0.6.1.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data
Code
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Medical History Table",
      dataname = "ADMH",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADMH, c("MHTERM", "MHDECOD")),
        selected = c("MHDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADMH, c("MHBODSYS", "MHSOC")),
        selected = "MHBODSYS"
      ),
      add_total = TRUE,
      event_type = "condition"
    )
  )
)

shinyApp(app$ui, app$server)

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

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

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://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 %>%
    filter(SAFFL == "Y")
  ADMH <- random.cdisc.data::cadmh %>%
    filter(SAFFL == "Y" & MHBODSYS != "" & MHDECOD != "")
})
datanames <- c("ADSL", "ADMH")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Medical History Table",
      dataname = "ADMH",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADMH, c("MHTERM", "MHDECOD")),
        selected = c("MHDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADMH, c("MHBODSYS", "MHSOC")),
        selected = "MHBODSYS"
      ),
      add_total = TRUE,
      event_type = "condition"
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-20 18:00:54 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     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

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

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