FDA Table 2

Baseline Demographic and Clinical Characteristics, Safety Population, Pooled Analyses

                                                  A: Drug X            B: Placebo         C: Combination      Total Population 
Characteristic                                     (N=134)              (N=134)              (N=132)              (N=400)      
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Sex                                                                                                                            
  F                                                79 (59%)            82 (61.2%)            70 (53%)           231 (57.8%)    
  M                                                55 (41%)            52 (38.8%)            62 (47%)           169 (42.2%)    
Age, years                                                                                                                     
  Mean (SD)                                       33.8 (6.6)           35.4 (7.9)           35.4 (7.7)           34.9 (7.4)    
  Median (Min - Max)                          33.0 (21.0 - 50.0)   35.0 (21.0 - 62.0)   35.0 (20.0 - 69.0)   34.0 (20.0 - 69.0)
Age Group, years                                                                                                               
  >=17 to <65                                     134 (100%)           134 (100%)          131 (99.2%)          399 (99.8%)    
  >=65                                                0                    0                 1 (0.8%)             1 (0.2%)     
Race                                                                                                                           
  ASIAN                                           68 (50.7%)            67 (50%)            73 (55.3%)           208 (52%)     
  BLACK OR AFRICAN AMERICAN                       31 (23.1%)           28 (20.9%)           32 (24.2%)           91 (22.8%)    
  WHITE                                           27 (20.1%)           26 (19.4%)           21 (15.9%)           74 (18.5%)    
  AMERICAN INDIAN OR ALASKA NATIVE                  8 (6%)             11 (8.2%)             6 (4.5%)            25 (6.2%)     
  MULTIPLE                                            0                 1 (0.7%)                0                 1 (0.2%)     
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER           0                 1 (0.7%)                0                 1 (0.2%)     
Ethnicity                                                                                                                      
  HISPANIC OR LATINO                              15 (11.2%)           18 (13.4%)           15 (11.4%)            48 (12%)     
  NOT HISPANIC OR LATINO                         104 (77.6%)          103 (76.9%)          101 (76.5%)           308 (77%)     
  NOT REPORTED                                     6 (4.5%)            10 (7.5%)            11 (8.3%)            27 (6.8%)     
  UNKNOWN                                          9 (6.7%)             3 (2.2%)             5 (3.8%)            17 (4.2%)     
Country of Participation                                                                                                       
  CHN                                             74 (55.2%)           81 (60.4%)           64 (48.5%)          219 (54.8%)    
  USA                                             10 (7.5%)            13 (9.7%)            17 (12.9%)            40 (10%)     
  BRA                                             13 (9.7%)             7 (5.2%)            10 (7.6%)            30 (7.5%)     
  PAK                                              12 (9%)              9 (6.7%)            10 (7.6%)            31 (7.8%)     
  NGA                                               8 (6%)              7 (5.2%)            11 (8.3%)            26 (6.5%)     
  RUS                                              5 (3.7%)              8 (6%)              6 (4.5%)            19 (4.8%)     
  JPN                                              5 (3.7%)              4 (3%)              9 (6.8%)            18 (4.5%)     
  GBR                                               4 (3%)              3 (2.2%)             2 (1.5%)             9 (2.2%)     
  CAN                                              3 (2.2%)             2 (1.5%)             3 (2.3%)              8 (2%)      
Baseline Temperature (C)                                                                                                       
  Mean (SD)                                       36.7 (1.0)           36.6 (1.1)           36.5 (1.0)           36.6 (1.0)    
  Median (Min - Max)                          36.7 (34.0 - 39.3)   36.6 (33.6 - 38.9)   36.5 (34.2 - 38.9)   36.6 (33.6 - 39.3)
# Load Libraries & Data
library(cardinal)
library(dplyr)

adsl <- random.cdisc.data::cadsl
advs <- random.cdisc.data::cadvs

# Pre-Processing - Add any variables needed in your table to df
adsl <- adsl %>%
  mutate(AGEGR1 = as.factor(case_when(
    AGE >= 17 & AGE < 65 ~ ">=17 to <65",
    AGE >= 65 ~ ">=65",
    AGE >= 65 & AGE < 75 ~ ">=65 to <75",
    AGE >= 75 ~ ">=75"
  )))

advs <- advs %>%
  filter(AVISIT == "BASELINE", VSTESTCD == "TEMP") %>%
  select("USUBJID", "AVAL")

anl <- left_join(adsl, advs, by = "USUBJID")

# Output Table
make_table_02(
  df = anl,
  vars = c("SEX", "AGE", "AGEGR1", "RACE", "ETHNIC", "COUNTRY", "AVAL"),
  lbl_vars = c(
    "Sex", "Age, years", "Age Group, years", "Race", "Ethnicity",
    "Country of Participation", "Baseline Temperature (C)"
  )
)

make_table_02()

Required variables:

  • df: The variables specified by vars, arm_var, and saffl_var.
  • alt_counts_df (if specified): USUBJID and the variables specified by arm_var and saffl_var.
Argument Description Default
df (data.frame) Dataset (typically ADSL) required to build table. No default
alt_counts_df (character) Alternative dataset used only to calculate column counts. NULL
show_colcounts (flag) Whether column counts should be printed. TRUE
arm_var (character) Arm variable used to split table into columns. "ARM"
saffl_var (character) Flag variable used to indicate inclusion in safety population. "SAFFL"
vars (character) Variables from df to include in the table. c("SEX", "AGE", "AGEGR1", "RACE", "ETHNIC", "COUNTRY")
lbl_vars (character) Labels corresponding to variables in vars to print in the table. Labels should be ordered according to the order of variables in vars. formatters::var_labels(df, fill = TRUE)[vars]
lbl_overall (character) If specified, an overall column will be added to the table with the given value as the column label. "Total Population"
.stats (character) Statistics to include in the table. Includes statistics for all variable types (only the statistics that are valid for a given variable’s type will be printed). See tern::analyze_vars() for options. c("mean_sd", "median_range", "count_fraction")
.formats (named list of character) List of formats corresponding to each value in .stats. Each name is a value in .stats and the corresponding value is the format that should be applied to that statistic. See formatters::list_valid_format_labels() for a list of valid formats. NULL
prune_0 (flag) Whether all-zero rows should be removed from the table. TRUE
na_rm (flag) Whether NA levels should be removed from the table. FALSE
annotations (named list of character) List of annotations to add to the table. Valid annotation types are title, subtitles, main_footer, and prov_footer. Each name-value pair should use the annotation type as name and the desired string as value. NULL

Source code for this function is available here.

A: Drug X (N=134) B: Placebo (N=134) C: Combination (N=132) Total Population (N=400)
Characteristic



  Sex



    F 79 (59.0%) 82 (61.2%) 70 (53.0%) 231 (57.8%)
    M 55 (41.0%) 52 (38.8%) 62 (47.0%) 169 (42.2%)
  Age, years



    Mean (SD) 33.8 ( 6.6) 35.4 ( 7.9) 35.4 ( 7.7) 34.9 ( 7.4)
    Median (Min - Max) 33.0 (21.0 - 50.0) 35.0 (21.0 - 62.0) 35.0 (20.0 - 69.0) 34.0 (20.0 - 69.0)
  Age Group, years



    >=17 to <65 134 (100.0%) 134 (100.0%) 131 (99.2%) 399 (99.8%)
    >=65  0   0   1 ( 0.8%)  1 ( 0.2%)
  Race



    ASIAN 68 (50.7%) 67 (50.0%) 73 (55.3%) 208 (52.0%)
    BLACK OR AFRICAN AMERICAN 31 (23.1%) 28 (20.9%) 32 (24.2%) 91 (22.8%)
    WHITE 27 (20.1%) 26 (19.4%) 21 (15.9%) 74 (18.5%)
    AMERICAN INDIAN OR ALASKA NATIVE  8 ( 6.0%) 11 ( 8.2%)  6 ( 4.5%) 25 ( 6.2%)
    MULTIPLE  0   1 ( 0.7%)  0   1 ( 0.2%)
    NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER  0   1 ( 0.7%)  0   1 ( 0.2%)
  Ethnicity



    HISPANIC OR LATINO 15 (11.2%) 18 (13.4%) 15 (11.4%) 48 (12.0%)
    NOT HISPANIC OR LATINO 104 (77.6%) 103 (76.9%) 101 (76.5%) 308 (77.0%)
    NOT REPORTED  6 ( 4.5%) 10 ( 7.5%) 11 ( 8.3%) 27 ( 6.8%)
    UNKNOWN  9 ( 6.7%)  3 ( 2.2%)  5 ( 3.8%) 17 ( 4.2%)
  Country of Participation



    CHN 74 (55.2%) 81 (60.4%) 64 (48.5%) 219 (54.8%)
    USA 10 ( 7.5%) 13 ( 9.7%) 17 (12.9%) 40 (10.0%)
    BRA 13 ( 9.7%)  7 ( 5.2%) 10 ( 7.6%) 30 ( 7.5%)
    PAK 12 ( 9.0%)  9 ( 6.7%) 10 ( 7.6%) 31 ( 7.8%)
    NGA  8 ( 6.0%)  7 ( 5.2%) 11 ( 8.3%) 26 ( 6.5%)
    RUS  5 ( 3.7%)  8 ( 6.0%)  6 ( 4.5%) 19 ( 4.8%)
    JPN  5 ( 3.7%)  4 ( 3.0%)  9 ( 6.8%) 18 ( 4.5%)
    GBR  4 ( 3.0%)  3 ( 2.2%)  2 ( 1.5%)  9 ( 2.2%)
    CAN  3 ( 2.2%)  2 ( 1.5%)  3 ( 2.3%)  8 ( 2.0%)
  Baseline Temperature (C)



    Mean (SD) 36.7 ( 1.0) 36.6 ( 1.1) 36.5 ( 1.0) 36.6 ( 1.0)
    Median (Min - Max) 36.7 (34.0 - 39.3) 36.6 (33.6 - 38.9) 36.5 (34.2 - 38.9) 36.6 (33.6 - 39.3) 
# Load Libraries & Data
library(cardinal)
library(dplyr)

adsl <- random.cdisc.data::cadsl
advs <- random.cdisc.data::cadvs

# Pre-Processing - Add any variables needed in your table to df
adsl <- adsl %>%
  mutate(AGEGR1 = as.factor(case_when(
    AGE >= 17 & AGE < 65 ~ ">=17 to <65",
    AGE >= 65 ~ ">=65",
    AGE >= 65 & AGE < 75 ~ ">=65 to <75",
    AGE >= 75 ~ ">=75"
  )))

advs <- advs %>%
  filter(AVISIT == "BASELINE", VSTESTCD == "TEMP") %>%
  select("USUBJID", "AVAL")

anl <- left_join(adsl, advs, by = "USUBJID")

# Output Table
make_table_02_tplyr(
  df = anl,
  vars = c("SEX", "AGE", "AGEGR1", "RACE", "ETHNIC", "COUNTRY", "AVAL"),
  lbl_vars = c(
    "Sex", "Age, years", "Age Group, years", "Race", "Ethnicity",
    "Country of Participation", "Baseline Temperature (C)"
  )
)

make_table_02_tplyr()

Required variables:

  • df: SAFFL and the variables specified by vars and arm_var.
  • alt_counts_df (if specified and tplyr_raw = FALSE): SAFFL, USUBJID, and the variable specified by arm_var.
Argument Description Default
df (data.frame) Dataset (typically ADSL) required to build table. No default
alt_counts_df (character) Alternative dataset used only to calculate column counts. NULL
show_colcounts (flag) Whether column counts should be printed. TRUE
arm_var (character) Arm variable used to split table into columns. "ARM"
saffl_var (character) Flag variable used to indicate inclusion in safety population. "SAFFL"
vars (character) Variables from df to include in the table. c("SEX", "AGE", "AGEGR1", "RACE", "ETHNIC", "COUNTRY")
lbl_vars (character) Labels corresponding to variables in vars to print in the table. Labels should be ordered according to the order of variables in vars. formatters::var_labels(df, fill = TRUE)[vars]
lbl_overall (character) If specified, an overall column will be added to the table with the given value as the column label. "Total Population"
prune_0 (flag) Whether all-zero rows should be removed from the table. TRUE
na_rm (flag) Whether NA levels should be removed from the table. FALSE
annotations (named list of character) List of annotations to add to the table. Valid annotation types are title and subtitles. Each name-value pair should use the annotation type as name and the desired string as value. NULL
tplyr_raw (flag) Whether the raw tibble created using Tplyr functions should be returned, or the table formatted using functions from tfrmt should be returned (default). FALSE

Source code for this function is available here.

Characteristic A: Drug X
N = 134		 B: Placebo
N = 134		 C: Combination
N = 132		 Total Population
N = 400
Sex, n (%)



    F 79 (59%) 82 (61%) 70 (53%) 231 (58%)
    M 55 (41%) 52 (39%) 62 (47%) 169 (42%)
Age, years



    Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7) 34.9 (7.4)
    Median (min - max) 33.0 (21.0 - 50.0) 35.0 (21.0 - 62.0) 35.0 (20.0 - 69.0) 34.0 (20.0 - 69.0)
Age Group years, n (%)



    ≥17 to &lt;65 134 (100%) 134 (100%) 131 (99%) 399 (100%)
    ≥65 0 (0%) 0 (0%) 1 (0.8%) 1 (0.3%)
Race, n (%)



    ASIAN 68 (51%) 67 (50%) 73 (55%) 208 (52%)
    BLACK OR AFRICAN AMERICAN 31 (23%) 28 (21%) 32 (24%) 91 (23%)
    WHITE 27 (20%) 26 (19%) 21 (16%) 74 (19%)
    AMERICAN INDIAN OR ALASKA NATIVE 8 (6.0%) 11 (8.2%) 6 (4.5%) 25 (6.3%)
    MULTIPLE 0 (0%) 1 (0.7%) 0 (0%) 1 (0.3%)
    NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 (0%) 1 (0.7%) 0 (0%) 1 (0.3%)
    OTHER 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    UNKNOWN 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Ethnicity, n (%)



    HISPANIC OR LATINO 15 (11%) 18 (13%) 15 (11%) 48 (12%)
    NOT HISPANIC OR LATINO 104 (78%) 103 (77%) 101 (77%) 308 (77%)
    NOT REPORTED 6 (4.5%) 10 (7.5%) 11 (8.3%) 27 (6.8%)
    UNKNOWN 9 (6.7%) 3 (2.2%) 5 (3.8%) 17 (4.3%)
Country of Participation, n (%)



    CHN 74 (55%) 81 (60%) 64 (48%) 219 (55%)
    USA 10 (7.5%) 13 (9.7%) 17 (13%) 40 (10%)
    BRA 13 (9.7%) 7 (5.2%) 10 (7.6%) 30 (7.5%)
    PAK 12 (9.0%) 9 (6.7%) 10 (7.6%) 31 (7.8%)
    NGA 8 (6.0%) 7 (5.2%) 11 (8.3%) 26 (6.5%)
    RUS 5 (3.7%) 8 (6.0%) 6 (4.5%) 19 (4.8%)
    JPN 5 (3.7%) 4 (3.0%) 9 (6.8%) 18 (4.5%)
    GBR 4 (3.0%) 3 (2.2%) 2 (1.5%) 9 (2.3%)
    CAN 3 (2.2%) 2 (1.5%) 3 (2.3%) 8 (2.0%)
    CHE 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Baseline Temperature (C)



    Mean (SD) 36.7 (1.0) 36.6 (1.1) 36.5 (1.0) 36.6 (1.0)
    Median (min - max) 36.7 (34.0 - 39.3) 36.6 (33.6 - 38.9) 36.5 (34.2 - 38.9) 36.6 (33.6 - 39.3) 
# Load Libraries & Data
library(cardinal)
library(dplyr)

adsl <- random.cdisc.data::cadsl
advs <- random.cdisc.data::cadvs

# Pre-Processing - Add any variables needed in your table to df
adsl <- adsl %>%
  mutate(AGEGR1 = as.factor(case_when(
    AGE >= 17 & AGE < 65 ~ "≥17 to &lt;65",
    AGE >= 65 ~ "≥65",
    AGE >= 65 & AGE < 75 ~ "≥65 to &lt;75",
    AGE >= 75 ~ "≥75"
  )))

advs <- advs %>%
  filter(AVISIT == "BASELINE", VSTESTCD == "TEMP") %>%
  select("USUBJID", "AVAL")

anl <- left_join(adsl, advs, by = "USUBJID")

# Output Table
make_table_02_gtsum(
  df = anl,
  vars = c("SEX", "AGE", "AGEGR1", "RACE", "ETHNIC", "COUNTRY", "AVAL"),
  lbl_vars = c(
    "Sex", "Age, years", "Age Group years", "Race", "Ethnicity",
    "Country of Participation", "Baseline Temperature (C)"
  )
)

make_table_02_gtsum()

Required variables:

  • df: SAFFL and the variables specified by vars and arm_var.
  • alt_counts_df (if specified and tplyr_raw = FALSE): SAFFL, USUBJID, and the variable specified by arm_var.
Argument Description Default
df (data.frame) Dataset (typically ADSL) required to build table. No default
alt_counts_df (character) Alternative dataset used only to calculate column counts. NULL
show_colcounts (flag) Whether column counts should be printed. TRUE
arm_var (character) Arm variable used to split table into columns. "ARM"
saffl_var (character) Flag variable used to indicate inclusion in safety population. "SAFFL"
vars (character) Variables from df to include in the table. c("SEX", "AGE", "AGEGR1", "RACE", "ETHNIC", "COUNTRY")
lbl_vars (character) Labels corresponding to variables in vars to print in the table. Labels should be ordered according to the order of variables in vars. formatters::var_labels(df, fill = TRUE)[vars]
lbl_overall (character) If specified, an overall column will be added to the table with the given value as the column label. "Total Population"

Source code for this function is available here.