benchmarks
benchmarks.RmdBenchmarking demographics data
arm_var <- "ARM"
vars <- c("AGE", "SEX", "RACE", "ETHNIC", "COUNTRY", "DTHFL", "BMRKR1",
"REGION1","BMRKR2")
bench::mark(
tern_dmg_tab <- dtlg::tern_summary_table(adsl_large, target = vars, treat = arm_var),
dtlg_dmg_tab <- dtlg::summary_table(adsl_large, target = vars, treat = arm_var, indent = '', .total_dt = adsl_large),
iterations = 1L,
check = FALSE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch> <bch:> <dbl> <bch:byt> <dbl>
#> 1 tern_dmg_tab <- dtlg::tern_summary_… 28s 28s 0.0357 7.94GB 1.39
#> 2 dtlg_dmg_tab <- dtlg::summary_table… 515ms 515ms 1.94 542.09MB 1.94
dtlg::as_dtlg_table(tt = tern_dmg_tab)
#> stats A: Drug X B: Placebo
#> <char> <char> <char>
#> 1: AGE
#> 2: n 333924 333087
#> 3: Mean (SD) 34.5 (7.1) 34.5 (7.1)
#> 4: Median 34.0 34.0
#> 5: Min - Max 20.0 - 84.0 20.0 - 86.0
#> 6: SEX
#> 7: n 333924 333087
#> 8: F 173527 (52%) 173167 (52%)
#> 9: M 160397 (48%) 159920 (48%)
#> 10: RACE
#> 11: n 333924 333087
#> 12: ASIAN 184009 (55.1%) 183379 (55.1%)
#> 13: BLACK OR AFRICAN AMERICAN 76493 (22.9%) 76700 (23%)
#> 14: WHITE 52983 (15.9%) 52948 (15.9%)
#> 15: AMERICAN INDIAN OR ALASKA NATIVE 16823 (5%) 16435 (4.9%)
#> 16: MULTIPLE 1367 (0.4%) 1304 (0.4%)
#> 17: NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 978 (0.3%) 1004 (0.3%)
#> 18: OTHER 615 (0.2%) 693 (0.2%)
#> 19: UNKNOWN 656 (0.2%) 624 (0.2%)
#> 20: ETHNIC
#> 21: n 333924 333087
#> 22: HISPANIC OR LATINO 33527 (10%) 33122 (9.9%)
#> 23: NOT HISPANIC OR LATINO 267178 (80%) 266445 (80%)
#> 24: NOT REPORTED 19788 (5.9%) 20149 (6%)
#> 25: UNKNOWN 13431 (4%) 13371 (4%)
#> 26: COUNTRY
#> 27: n 333924 333087
#> 28: CHN 168262 (50.4%) 168213 (50.5%)
#> 29: USA 41035 (12.3%) 40620 (12.2%)
#> 30: BRA 25981 (7.8%) 25698 (7.7%)
#> 31: PAK 26117 (7.8%) 26043 (7.8%)
#> 32: NGA 25230 (7.6%) 25404 (7.6%)
#> 33: RUS 17699 (5.3%) 17587 (5.3%)
#> 34: JPN 15347 (4.6%) 15475 (4.6%)
#> 35: GBR 8567 (2.6%) 8284 (2.5%)
#> 36: CAN 4738 (1.4%) 4783 (1.4%)
#> 37: CHE 948 (0.3%) 980 (0.3%)
#> 38: DTHFL
#> 39: n 333924 333087
#> 40: N 278284 (83.3%) 277476 (83.3%)
#> 41: Y 55640 (16.7%) 55611 (16.7%)
#> 42: BMRKR1
#> 43: n 333924 333087
#> 44: Mean (SD) 6.0 (3.5) 6.0 (3.5)
#> 45: Median 5.3 5.3
#> 46: Min - Max 0.0 - 36.4 0.0 - 33.5
#> 47: REGION1
#> 48: n 333924 333087
#> 49: Africa 25230 (7.6%) 25404 (7.6%)
#> 50: Asia 209726 (62.8%) 209731 (63%)
#> 51: Eurasia 17699 (5.3%) 17587 (5.3%)
#> 52: Europe 8567 (2.6%) 8284 (2.5%)
#> 53: North America 45773 (13.7%) 45403 (13.6%)
#> 54: South America 25981 (7.8%) 25698 (7.7%)
#> 55: Missing 948 (0.3%) 980 (0.3%)
#> 56: BMRKR2
#> 57: n 333924 333087
#> 58: LOW 111579 (33.4%) 111021 (33.3%)
#> 59: MEDIUM 111381 (33.4%) 111000 (33.3%)
#> 60: HIGH 110964 (33.2%) 111066 (33.3%)
#> stats A: Drug X B: Placebo
#> C: Combination
#> <char>
#> 1:
#> 2: 332989
#> 3: 34.5 (7.1)
#> 4: 34.0
#> 5: 20.0 - 89.0
#> 6:
#> 7: 332989
#> 8: 172556 (51.8%)
#> 9: 160433 (48.2%)
#> 10:
#> 11: 332989
#> 12: 183398 (55.1%)
#> 13: 76367 (22.9%)
#> 14: 53015 (15.9%)
#> 15: 16551 (5%)
#> 16: 1294 (0.4%)
#> 17: 1054 (0.3%)
#> 18: 644 (0.2%)
#> 19: 666 (0.2%)
#> 20:
#> 21: 332989
#> 22: 33604 (10.1%)
#> 23: 266303 (80%)
#> 24: 19896 (6%)
#> 25: 13186 (4%)
#> 26:
#> 27: 332989
#> 28: 168093 (50.5%)
#> 29: 40864 (12.3%)
#> 30: 25833 (7.8%)
#> 31: 25885 (7.8%)
#> 32: 25438 (7.6%)
#> 33: 17359 (5.2%)
#> 34: 15522 (4.7%)
#> 35: 8419 (2.5%)
#> 36: 4593 (1.4%)
#> 37: 983 (0.3%)
#> 38:
#> 39: 332989
#> 40: 277339 (83.3%)
#> 41: 55650 (16.7%)
#> 42:
#> 43: 332989
#> 44: 6.0 (3.5)
#> 45: 5.4
#> 46: 0.0 - 34.2
#> 47:
#> 48: 332989
#> 49: 25438 (7.6%)
#> 50: 209500 (62.9%)
#> 51: 17359 (5.2%)
#> 52: 8419 (2.5%)
#> 53: 45457 (13.7%)
#> 54: 25833 (7.8%)
#> 55: 983 (0.3%)
#> 56:
#> 57: 332989
#> 58: 110749 (33.3%)
#> 59: 111125 (33.4%)
#> 60: 111115 (33.4%)
#> C: Combination
dtlg_dmg_tab
#> stats A: Drug X B: Placebo
#> <char> <char> <char>
#> 1: AGE
#> 2: n 333924 333087
#> 3: Mean (SD) 34.5 (7.1) 34.5 (7.1)
#> 4: Median 34 34
#> 5: Min, Max 20.0, 84.0 20.0, 86.0
#> 6: Missing 0 0
#> 7: SEX
#> 8: F 173527 (52.0%) 173167 (52.0%)
#> 9: M 160397 (48.0%) 159920 (48.0%)
#> 10: RACE
#> 11: AMERICAN INDIAN OR ALASKA NATIVE 16823 (5.0%) 16435 (4.9%)
#> 12: ASIAN 184009 (55.1%) 183379 (55.1%)
#> 13: BLACK OR AFRICAN AMERICAN 76493 (22.9%) 76700 (23.0%)
#> 14: MULTIPLE 1367 (0.4%) 1304 (0.4%)
#> 15: NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 978 (0.3%) 1004 (0.3%)
#> 16: OTHER 615 (0.2%) 693 (0.2%)
#> 17: UNKNOWN 656 (0.2%) 624 (0.2%)
#> 18: WHITE 52983 (15.9%) 52948 (15.9%)
#> 19: ETHNIC
#> 20: HISPANIC OR LATINO 33527 (10.0%) 33122 (9.9%)
#> 21: NOT HISPANIC OR LATINO 267178 (80.0%) 266445 (80.0%)
#> 22: NOT REPORTED 19788 (5.9%) 20149 (6.0%)
#> 23: UNKNOWN 13431 (4.0%) 13371 (4.0%)
#> 24: COUNTRY
#> 25: BRA 25981 (7.8%) 25698 (7.7%)
#> 26: CAN 4738 (1.4%) 4783 (1.4%)
#> 27: CHE 948 (0.3%) 980 (0.3%)
#> 28: CHN 168262 (50.4%) 168213 (50.5%)
#> 29: GBR 8567 (2.6%) 8284 (2.5%)
#> 30: JPN 15347 (4.6%) 15475 (4.6%)
#> 31: NGA 25230 (7.6%) 25404 (7.6%)
#> 32: PAK 26117 (7.8%) 26043 (7.8%)
#> 33: RUS 17699 (5.3%) 17587 (5.3%)
#> 34: USA 41035 (12.3%) 40620 (12.2%)
#> 35: DTHFL
#> 36: N 278284 (83.3%) 277476 (83.3%)
#> 37: Y 55640 (16.7%) 55611 (16.7%)
#> 38: BMRKR1
#> 39: n 333924 333087
#> 40: Mean (SD) 6.0 (3.5) 6.0 (3.5)
#> 41: Median 5.3 5.3
#> 42: Min, Max 0.0, 36.4 0.0, 33.5
#> 43: Missing 0 0
#> 44: REGION1
#> 45: Africa 25230 (7.6%) 25404 (7.6%)
#> 46: Asia 209726 (62.8%) 209731 (63.0%)
#> 47: Eurasia 17699 (5.3%) 17587 (5.3%)
#> 48: Europe 8567 (2.6%) 8284 (2.5%)
#> 49: North America 45773 (13.7%) 45403 (13.6%)
#> 50: South America 25981 (7.8%) 25698 (7.7%)
#> 51: BMRKR2
#> 52: HIGH 110964 (33.2%) 111066 (33.3%)
#> 53: LOW 111579 (33.4%) 111021 (33.3%)
#> 54: MEDIUM 111381 (33.4%) 111000 (33.3%)
#> stats A: Drug X B: Placebo
#> C: Combination
#> <char>
#> 1:
#> 2: 332989
#> 3: 34.5 (7.1)
#> 4: 34
#> 5: 20.0, 89.0
#> 6: 0
#> 7:
#> 8: 172556 (51.8%)
#> 9: 160433 (48.2%)
#> 10:
#> 11: 16551 (5.0%)
#> 12: 183398 (55.1%)
#> 13: 76367 (22.9%)
#> 14: 1294 (0.4%)
#> 15: 1054 (0.3%)
#> 16: 644 (0.2%)
#> 17: 666 (0.2%)
#> 18: 53015 (15.9%)
#> 19:
#> 20: 33604 (10.1%)
#> 21: 266303 (80.0%)
#> 22: 19896 (6.0%)
#> 23: 13186 (4.0%)
#> 24:
#> 25: 25833 (7.8%)
#> 26: 4593 (1.4%)
#> 27: 983 (0.3%)
#> 28: 168093 (50.5%)
#> 29: 8419 (2.5%)
#> 30: 15522 (4.7%)
#> 31: 25438 (7.6%)
#> 32: 25885 (7.8%)
#> 33: 17359 (5.2%)
#> 34: 40864 (12.3%)
#> 35:
#> 36: 277339 (83.3%)
#> 37: 55650 (16.7%)
#> 38:
#> 39: 332989
#> 40: 6.0 (3.5)
#> 41: 5.4
#> 42: 0.0, 34.2
#> 43: 0
#> 44:
#> 45: 25438 (7.6%)
#> 46: 209500 (62.9%)
#> 47: 17359 (5.2%)
#> 48: 8419 (2.5%)
#> 49: 45457 (13.7%)
#> 50: 25833 (7.8%)
#> 51:
#> 52: 111115 (33.4%)
#> 53: 110749 (33.3%)
#> 54: 111125 (33.4%)
#> C: CombinationBenchmarking AET01
arm_var <- "ARM"
aesi_vars = c("FATAL", "SER", "SERWD", "SERDSM", "RELSER",
"WD", "DSM", "REL", "RELWD", "RELDSM", "SEV")
bench::mark(
tern_safety_tab <- dtlg::tern_AET01_table(
adsl = adsl_small,
adae = aesi,
patient_var = "USUBJID",
treat_var = "ARM",
aesi_vars = aesi_vars
),
dtlg_safety_tab <- dtlg::AET01_table(
adsl = adsl_small,
adae = aesi,
patient_var = "USUBJID",
treat_var = "ARM",
aesi_vars = aesi_vars
),
iterations = 1L,
check = FALSE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch> <bch:> <dbl> <bch:byt> <dbl>
#> 1 tern_safety_tab <- dtlg::tern_AET01… 537ms 537ms 1.86 137.5MB 1.86
#> 2 dtlg_safety_tab <- dtlg::AET01_tabl… 120ms 120ms 8.35 28.8MB 0