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MHT01 Medical History Table.

Source: R/mht01.R
mht01.Rd

The MHT01 table provides an overview of the subjects medical history by SOC and Preferred Term.

Usage 

mht01_label

mht01_main(
  adam_db,
  arm_var = "ARM",
  row_split_var = "MHBODSYS",
  lbl_overall = NULL,
  summary_labels = list(all = mht01_label),
  ...
)

mht01_pre(adam_db, ...)

mht01_post(tlg, prune_0 = TRUE, ...)

mht01

Format

An object of class character of length 2.

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

row_split_var

(character) additional row split variables.

lbl_overall

(string) label used for overall column, if set to NULL the overall column is omitted

summary_labels

(list) of summarize labels. See details.

...

not used.

tlg

(TableTree, Listing or ggplot) object typically produced by a main function.

prune_0

(flag) remove 0 count rows

Value

the main function returns an rtables object.

the preprocessing function returns a list of data.frame.

the postprocessing function returns an rtables object or an ElementaryTable (null report).

Details

  • Numbers represent absolute numbers of patients and fraction of N, or absolute number of event when specified.

  • Remove zero-count rows unless overridden with prune_0 = FALSE.

  • Split columns by arm.

  • Does not include a total column by default.

  • Order by body system alphabetically and within body system and medical condition by decreasing total number of patients with the specific condition. summary_labels is used to control the summary for each level. If "all" is used, then each split will have that summary statistic with the labels. One special case is "TOTAL", this is for the overall population.

Functions

  • mht01_label: Default labels

  • mht01_main(): Main TLG function

  • mht01_pre(): Preprocessing

  • mht01_post(): Postprocessing

Note

  • adam_db object must contain an admh table with columns "MHBODSYS" and "MHDECOD".

Examples 

run(mht01, syn_data)
#>   MedDRA System Organ Class                                A: Drug X    B: Placebo   C: Combination
#>     MedDRA Preferred Term                                    (N=15)       (N=15)         (N=15)    
#>   —————————————————————————————————————————————————————————————————————————————————————————————————
#>   Total number of patients with at least one condition     13 (86.7%)   14 (93.3%)     15 (100%)   
#>   Total number of conditions                                   58           59             99      
#>   cl A                                                                                             
#>     Total number of patients with at least one condition   7 (46.7%)    6 (40.0%)      10 (66.7%)  
#>     Total number of conditions                                 8            11             16      
#>     trm A_2/2                                              5 (33.3%)    6 (40.0%)      6 (40.0%)   
#>     trm A_1/2                                              3 (20.0%)     1 (6.7%)      6 (40.0%)   
#>   cl B                                                                                             
#>     Total number of patients with at least one condition   12 (80.0%)   11 (73.3%)     12 (80.0%)  
#>     Total number of conditions                                 24           21             32      
#>     trm B_3/3                                              8 (53.3%)    6 (40.0%)      7 (46.7%)   
#>     trm B_1/3                                              5 (33.3%)    6 (40.0%)      8 (53.3%)   
#>     trm B_2/3                                              5 (33.3%)    6 (40.0%)      5 (33.3%)   
#>   cl C                                                                                             
#>     Total number of patients with at least one condition   8 (53.3%)    6 (40.0%)      11 (73.3%)  
#>     Total number of conditions                                 10           13             22      
#>     trm C_2/2                                              6 (40.0%)    4 (26.7%)      8 (53.3%)   
#>     trm C_1/2                                              4 (26.7%)    4 (26.7%)      5 (33.3%)   
#>   cl D                                                                                             
#>     Total number of patients with at least one condition   10 (66.7%)   7 (46.7%)      13 (86.7%)  
#>     Total number of conditions                                 16           14             29      
#>     trm D_1/3                                              4 (26.7%)    4 (26.7%)      7 (46.7%)   
#>     trm D_2/3                                              6 (40.0%)    2 (13.3%)      7 (46.7%)   
#>     trm D_3/3                                              2 (13.3%)    5 (33.3%)      7 (46.7%)