MHT01

Medical History

---

Standard Table

Preview

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%)  

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 History Prior to Study

Preview

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%)   

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 Total Number of Conditions Suppressed

Preview

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%)  

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 Total Number of Conditions Per Body System After The Summary of Patients

Not currently supported by rtables. Users, please notify us if this variation is important to you.

Table Showing Additional “All Patients” Column

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

Preview

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%) 

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(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

Preview

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)

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, {
  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] "2024-11-02 18:01:28 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
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 Matrix                  1.7-0       2024-04-26 [1] CRAN (R 4.4.1)
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 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)
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 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

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