DMT01 Table 1 (Default) Demographics and Baseline Characteristics Table 1.

For each variable, summary statistics are by default based on the number of patients in the corresponding n row.

Usage

dmt01_main(
  adam_db,
  arm_var = "ARM",
  lbl_overall = "All {Patient_label}",
  summaryvars = c("AAGE", "AGEGR1", "SEX", "ETHNIC", "RACE"),
  stats = list(default = c("n", "mean_sd", "median", "range", "count_fraction")),
  precision = list(),
  ...
)

dmt01_pre(adam_db, ...)

dmt01_post(tlg, prune_0 = TRUE, ...)

dmt01

Format

An object of class chevron_t of length 1.

Arguments

adam_db: (list of data.frames) object containing the ADaM datasets
arm_var: (string) variable used for column splitting
lbl_overall: (string) label used for overall column, if set to NULL the overall column is omitted
summaryvars: (character) variables summarized in demographic table. The label attribute of the corresponding column in adsl table of adam_db is used as label.
stats: (named list of character) where names of columns found in .df_row and the values indicate the statistical analysis to perform. If default is set, and parameter precision not specified, the value for default will be used.
precision: (named list of integer) where names are strings found in summaryvars and the values indicate the number of digits in statistics for numeric variables. If default is set, and parameter precision not specified, the value for default will be used. If neither are provided, auto determination is used. See tern::format_auto.
...: not used.
tlg: (TableTree, Listing or ggplot) object typically produced by a main function.
prune_0: (flag) remove 0 count rows

Details

Information from ADSUB are generally included into ADSL before analysis.
Default demographic and characteristics table
If not specified otherwise, numbers represent absolute numbers of patients and fraction of N
Remove zero-count rows
Split columns by arm (planned or actual / code or description)
Include a total column by default

Functions

dmt01_main(): Main TLG function
dmt01_pre(): Preprocessing
dmt01_post(): Postprocessing

Note

adam_db object must contain an adsl table with the columns specified in summaryvars.

Examples

run(dmt01, syn_data)
#>                                         A: Drug X    B: Placebo    C: Combination   All Patients
#>                                          (N=134)       (N=134)        (N=132)         (N=400)   
#>   ——————————————————————————————————————————————————————————————————————————————————————————————
#>   Age (yr)                                                                                      
#>     n                                      134           134            132             400     
#>     Mean (SD)                          33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) 
#>     Median                                33.0          35.0            35.0            34.0    
#>     Min - Max                            21 - 50       21 - 62        20 - 69         20 - 69   
#>   Age Group                                                                                     
#>     n                                      134           134            132             400     
#>     <65                                134 (100%)    134 (100%)     131 (99.2%)     399 (99.8%) 
#>     >=65                                    0             0           1 (0.8%)        1 (0.2%)  
#>   Sex                                                                                           
#>     n                                      134           134            132             400     
#>     Male                               55 (41.0%)    52 (38.8%)      62 (47.0%)     169 (42.2%) 
#>     Female                             79 (59.0%)    82 (61.2%)      70 (53.0%)     231 (57.8%) 
#>   Ethnicity                                                                                     
#>     n                                      134           134            132             400     
#>     NOT REPORTED                        6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  
#>     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%) 
#>     UNKNOWN                             9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  
#>   RACE                                                                                          
#>     n                                      134           134            132             400     
#>     AMERICAN INDIAN OR ALASKA NATIVE    8 (6.0%)      11 (8.2%)       6 (4.5%)       25 (6.2%)  
#>     ASIAN                              68 (50.7%)    68 (50.7%)      73 (55.3%)     209 (52.2%) 
#>     BLACK OR AFRICAN AMERICAN          31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) 
#>     WHITE                              27 (20.1%)    27 (20.1%)      21 (15.9%)      75 (18.8%)