Summarize medical history by system organ class (SOC) and preferred term
(PT). ADSL defines both the analysis population and the treatment
denominators. ADMH records for subjects not present in ADSL are excluded,
and treatment is derived from ADSL. If ADMH also contains arm, its
non-missing values must agree with ADSL for the same subject.
Arguments
- adsl
Subject-level analysis dataset. It must contain one row per non-missing
USUBJIDand the treatment variable named byarm.- admh
Medical history analysis dataset. It must contain
USUBJID,MHBODSYS, andMHDECOD.- arm
Name of the treatment variable in
adsl, character scalar;"TRT01A"by default.- add_all_patients_col
Logical scalar indicating whether an additional
All Patientscolumn is displayed.
Details
Counts for the overall table and each SOC include the number and percentage
of unique subjects with at least one condition and, on a separate row, the
non-unique number of condition records. PT rows contain unique subject
counts and percentages. SOCs and PTs are sorted by decreasing total unique
subject count, with labels used to break ties deterministically. Missing or
blank SOC and PT values are displayed as <Missing>.
Note
* Default arm variables are set to `"TRT01A"` for safety output, and `"TRT01P"` for efficacy output
References
The table structure follows the public MHT01 example in the TLG Catalog.
Examples
library(dplyr)
adsl <- eg_adsl %>%
dplyr::mutate(TRT01A = factor(TRT01A))
admh <- eg_admh
out <- t_mh_slide(adsl, admh)
print(out)
#> Medical History
#>
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> 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 condition 116 (86.6%) 120 (89.6%) 120 (90.9%) 356 (89.0%)
#> Total number of conditions 618 598 703 1919
#> cl B
#> Total number of patients with at least one condition 92 (68.7%) 90 (67.2%) 94 (71.2%) 276 (69.0%)
#> Total number of conditions 182 187 200 569
#> trm B_3/3 45 (33.6%) 46 (34.3%) 54 (40.9%) 145 (36.2%)
#> trm B_1/3 56 (41.8%) 46 (34.3%) 42 (31.8%) 144 (36.0%)
#> trm B_2/3 44 (32.8%) 45 (33.6%) 49 (37.1%) 138 (34.5%)
#> cl D
#> Total number of patients with at least one condition 92 (68.7%) 86 (64.2%) 95 (72.0%) 273 (68.2%)
#> Total number of conditions 188 189 199 576
#> trm D_2/3 46 (34.3%) 51 (38.1%) 51 (38.6%) 148 (37.0%)
#> trm D_1/3 46 (34.3%) 50 (37.3%) 51 (38.6%) 147 (36.8%)
#> trm D_3/3 51 (38.1%) 39 (29.1%) 46 (34.8%) 136 (34.0%)
#> cl A
#> Total number of patients with at least one condition 81 (60.4%) 74 (55.2%) 83 (62.9%) 238 (59.5%)
#> Total number of conditions 129 104 144 377
#> trm A_1/2 59 (44.0%) 47 (35.1%) 54 (40.9%) 160 (40.0%)
#> trm A_2/2 43 (32.1%) 42 (31.3%) 51 (38.6%) 136 (34.0%)
#> cl C
#> Total number of patients with at least one condition 74 (55.2%) 72 (53.7%) 85 (64.4%) 231 (57.8%)
#> Total number of conditions 119 118 160 397
#> trm C_1/2 51 (38.1%) 45 (33.6%) 56 (42.4%) 152 (38.0%)
#> trm C_2/2 42 (31.3%) 45 (33.6%) 59 (44.7%) 146 (36.5%)
out_without_overall <- t_mh_slide(
adsl,
admh,
add_all_patients_col = FALSE
)