RMPT06

Seriousness, Outcomes, Severity, Frequency with 95% CI for Risk Management Plan

Please note that by default the percentage analysis uses Wald’s confidence interval with continuity correction and the default confidence interval for percentage is 95%. To use a different method for confidence interval calculation, the method name needs to be provided to method argument.

Code
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  estimate_proportion(
    vars = "AEFL",
    method = "clopper-pearson",
    .labels = c(
      n_prop = "Number of patients with at least one adverse event",
      prop_ci = "95% CI for % of patients with at least one AE (Clopper-Pearson)"
    ),
    table_names = "est_prop"
  )

result_adsl <- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_values(
    "STUDYID",
    values = as.character(unique(adae$STUDYID)),
    .stats = "count",
    .labels = c(count = "Total number of AEs"),
    table_names = "total_aes"
  ) %>%
  count_occurrences(
    "WTOXGR",
    var_labels = "Number of patients with at least one AE by worst grade",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "fl_ser"
  ) %>%
  count_occurrences(
    "AEOUT",
    denom = "n",
    var_labels = "Number of patients with at least one AE by outcome",
    show_labels = "visible"
  )

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

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adsl,
  result_adae[seq_len(nrow(result_adae)), ]
)

result
                                                                   A: Drug X      B: Placebo    C: Combination
                                                                    (N=134)        (N=134)         (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————————————
Number of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  
95% CI for % of patients with at least one AE (Clopper-Pearson)   (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) 
Total number of AEs                                                   609            622             703      
Number of patients with at least one AE by worst grade                                                        
  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   
  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   
  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  
  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  
  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  
Number of patients with at least one serious AE                   104 (85.2%)    101 (82.1%)      99 (82.5%)  
Number of patients with at least one AE by outcome                                                            
  Fatal outcome                                                    76 (62.3%)     70 (56.9%)      75 (62.5%)  
  Unresolved                                                       66 (54.1%)     64 (52.0%)      70 (58.3%)  
  Recovered/Resolved                                               73 (59.8%)     83 (67.5%)      88 (73.3%)  
  Resolved with sequelae                                           44 (36.1%)     40 (32.5%)      43 (35.8%)  
  Recovering/Resolving                                             81 (66.4%)     84 (68.3%)      88 (73.3%)  
  Unknown outcome                                                  38 (31.1%)     49 (39.8%)      44 (36.7%)

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Code
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  estimate_proportion(
    vars = "AEFL",
    method = "clopper-pearson",
    .labels = c(
      n_prop = "Number of patients with at least one adverse event",
      prop_ci = "95% CI for % of patients with at least one AE (Clopper-Pearson)"
    ),
    table_names = "est_prop"
  )

result_adsl <- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_values(
    "STUDYID",
    values = as.character(unique(adae$STUDYID)),
    .stats = "count",
    .labels = c(count = "Total number of AEs"),
    table_names = "total_aes"
  ) %>%
  count_occurrences(
    "WTOXGR",
    var_labels = "Number of patients with at least one AE by worst grade",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "fl_ser",
    denom = "N_col"
  )

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

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adsl,
  result_adae[seq_len(nrow(result_adae)), ]
)

result
                                                                   A: Drug X      B: Placebo    C: Combination
                                                                    (N=134)        (N=134)         (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————————————
Number of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  
95% CI for % of patients with at least one AE (Clopper-Pearson)   (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) 
Total number of AEs                                                   609            622             703      
Number of patients with at least one AE by worst grade                                                        
  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   
  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   
  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  
  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  
  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  
Number of patients with at least one serious AE                   104 (77.6%)    101 (75.4%)      99 (75.0%)

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.

Please note that by default the percentage difference analysis also uses Wald’s confidence interval with continuity correction and the default confidence interval is 95%.

Code
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", ref_group = "A: Drug X") %>%
  estimate_proportion(
    vars = "AEFL",
    method = "clopper-pearson",
    .labels = c(
      n_prop = "Number of patients with at least one adverse event",
      prop_ci = "95% CI for % of patients with at least one AE"
    ),
    table_names = "est_prop"
  ) %>%
  estimate_proportion_diff(
    vars = "AEFL",
    .labels = c(
      diff = "Difference in % of patients with at least one AE",
      diff_ci = "95% CI of difference (Wald, with correction)"
    ),
    table_names = "est_prop_diff"
  )

result_adsl <- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_values(
    "STUDYID",
    values = as.character(unique(adae$STUDYID)),
    .stats = "count",
    .labels = c(count = "Total number of AEs"),
    table_names = "total_aes"
  ) %>%
  count_occurrences(
    "WTOXGR",
    var_labels = "Number of patients with at least one AE by worst grade",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "fl_ser",
    denom = "N_col"
  ) %>%
  count_occurrences(
    "AEOUT",
    denom = "n",
    var_labels = "Number of patients with at least one AE by outcome",
    show_labels = "visible"
  )

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

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adsl,
  result_adae[seq_len(nrow(result_adae)), ]
)

result
                                                          A: Drug X      B: Placebo    C: Combination
                                                           (N=134)        (N=134)         (N=132)    
—————————————————————————————————————————————————————————————————————————————————————————————————————
Number of patients with at least one adverse event       122 (91.0%)    123 (91.8%)     120 (90.9%)  
95% CI for % of patients with at least one AE            (84.9, 95.3)   (85.8, 95.8)    (84.7, 95.2) 
Difference in % of patients with at least one AE                            0.7             -0.1     
  95% CI of difference (Wald, with correction)                          (-6.7, 8.2)     (-7.8, 7.5)  
Total number of AEs                                          609            622             703      
Number of patients with at least one AE by worst grade                                               
  Grade 1                                                  7 (5.2%)       9 (6.7%)        4 (3.0%)   
  Grade 2                                                  6 (4.5%)      10 (7.5%)        7 (5.3%)   
  Grade 3                                                 18 (13.4%)     14 (10.4%)      16 (12.1%)  
  Grade 4                                                 15 (11.2%)     20 (14.9%)      18 (13.6%)  
  Grade 5                                                 76 (56.7%)     70 (52.2%)      75 (56.8%)  
Number of patients with at least one serious AE          104 (77.6%)    101 (75.4%)      99 (75.0%)  
Number of patients with at least one AE by outcome                                                   
  Fatal outcome                                           76 (62.3%)     70 (56.9%)      75 (62.5%)  
  Unresolved                                              66 (54.1%)     64 (52.0%)      70 (58.3%)  
  Recovered/Resolved                                      73 (59.8%)     83 (67.5%)      88 (73.3%)  
  Resolved with sequelae                                  44 (36.1%)     40 (32.5%)      43 (35.8%)  
  Recovering/Resolving                                    81 (66.4%)     84 (68.3%)      88 (73.3%)  
  Unknown outcome                                         38 (31.1%)     49 (39.8%)      44 (36.7%)

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 confidence level can be changed through the conf_level argument.

Code
lyt_adsl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", ref_group = "A: Drug X") %>%
  estimate_proportion(
    vars = "AEFL",
    conf_level = 0.90,
    method = "clopper-pearson",
    .labels = c(
      n_prop = "Number of patients with at least one adverse event",
      prop_ci = "90% CI for % of patients with at least one AE (Clopper-Pearson)"
    ),
    table_names = "est_prop"
  ) %>%
  estimate_proportion_diff(
    vars = "AEFL",
    conf_level = 0.90,
    method = "newcombe",
    .labels = c(
      diff = "Difference in % of patients with at least one AE",
      diff_ci = "90% CI of difference (Newcombe)"
    ),
    table_names = "est_prop_diff"
  )

result_adsl <- build_table(lyt_adsl, df = adsl1, alt_counts_df = adsl1)

# Layout for variables from adae dataset.
lyt_adae <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  count_values(
    "STUDYID",
    values = as.character(unique(adae$STUDYID)),
    .stats = "count",
    .labels = c(count = "Total number of AEs"),
    table_names = "total_aes"
  ) %>%
  count_occurrences(
    "WTOXGR",
    var_labels = "Number of patients with at least one AE by worst grade",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "fl_ser",
    denom = "N_col"
  ) %>%
  count_occurrences(
    "AEOUT",
    denom = "n",
    var_labels = "Number of patients with at least one AE by outcome",
    show_labels = "visible"
  )

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

# Combine tables.
col_info(result_adsl) <- col_info(result_adae)
result <- rbind(
  result_adsl,
  result_adae[seq_len(nrow(result_adae)), ]
)

result
                                                                   A: Drug X      B: Placebo    C: Combination
                                                                    (N=134)        (N=134)         (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————————————————————————
Number of patients with at least one adverse event                122 (91.0%)    123 (91.8%)     120 (90.9%)  
90% CI for % of patients with at least one AE (Clopper-Pearson)   (85.9, 94.8)   (86.8, 95.3)    (85.7, 94.7) 
Difference in % of patients with at least one AE                                     0.7             -0.1     
  90% CI of difference (Newcombe)                                                (-5.1, 6.6)     (-6.1, 5.8)  
Total number of AEs                                                   609            622             703      
Number of patients with at least one AE by worst grade                                                        
  Grade 1                                                           7 (5.2%)       9 (6.7%)        4 (3.0%)   
  Grade 2                                                           6 (4.5%)      10 (7.5%)        7 (5.3%)   
  Grade 3                                                          18 (13.4%)     14 (10.4%)      16 (12.1%)  
  Grade 4                                                          15 (11.2%)     20 (14.9%)      18 (13.6%)  
  Grade 5                                                          76 (56.7%)     70 (52.2%)      75 (56.8%)  
Number of patients with at least one serious AE                   104 (77.6%)    101 (75.4%)      99 (75.0%)  
Number of patients with at least one AE by outcome                                                            
  Fatal outcome                                                    76 (62.3%)     70 (56.9%)      75 (62.5%)  
  Unresolved                                                       66 (54.1%)     64 (52.0%)      70 (58.3%)  
  Recovered/Resolved                                               73 (59.8%)     83 (67.5%)      88 (73.3%)  
  Resolved with sequelae                                           44 (36.1%)     40 (32.5%)      43 (35.8%)  
  Recovering/Resolving                                             81 (66.4%)     84 (68.3%)      88 (73.3%)  
  Unknown outcome                                                  38 (31.1%)     49 (39.8%)      44 (36.7%)

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

Code
# In progress

The adae and adsl datasets need to undergo data processing before table production. Some new variables are added to these datasets to create the desired tables.

Code
library(tern)
library(dplyr)

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

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

df_max <- aggregate(as.numeric(AETOXGR) ~ USUBJID, data = adae, FUN = max, drop = FALSE)
colnames(df_max) <- c("USUBJID", "WTOXGR")

adae <- adae %>%
  left_join(df_max, by = c("USUBJID")) %>%
  mutate(
    WTOXGR = factor(WTOXGR, levels = c("1", "2", "3", "4", "5")),
    fl_ser = AESER == "Y"
  ) %>%
  mutate(
    WTOXGR = forcats::fct_recode(
      WTOXGR,
      "Grade 1" = "1",
      "Grade 2" = "2",
      "Grade 3" = "3",
      "Grade 4" = "4",
      "Grade 5" = "5"
    ),
    AEOUT = forcats::fct_recode(
      AEOUT,
      "Fatal outcome" = "FATAL",
      "Unresolved" = "NOT RECOVERED/NOT RESOLVED",
      "Recovered/Resolved" = "RECOVERED/RESOLVED",
      "Resolved with sequelae" = "RECOVERED/RESOLVED WITH SEQUELAE",
      "Recovering/Resolving" = "RECOVERING/RESOLVING",
      "Unknown outcome" = "UNKNOWN"
    )
  ) %>%
  var_relabel(
    WTOXGR = "Worst overall grade",
    fl_ser = "Number of patients with at least one serious AE"
  )

# Add AE flag to adsl.
adsl1 <- adsl %>%
  mutate(AEFL = ifelse(USUBJID %in% adae$USUBJID, TRUE, FALSE)) %>%
  var_relabel(AEFL = "At least one AE")

Reproducibility

Timestamp

[1] "2024-11-02 18:11:24 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 ───────────────────────────────────────────────────────────────────
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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