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An R package repository for pharma tables, listings and graphs developed using data table

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dtlg

The goal of dtlg is to create pharmaceutical tables, listings and graphs for use at scale by utilising data.table as a back-end for processing. Currently only tables can be created but the functions should encompass a large number of possibilities for pharma tables.

The package was created using TLGcatalog to create examples.

Advance formatting options to follow in future.

Installation

You can install the development version of dtlg from GitHub with:

# install.packages("devtools")
devtools::install_github("AscentSoftware/dtlg")

Benchmarks

The following benchmarks compare dtlg with tern and use the TLGcatalog to get table code for tern.

DMT01

DMT01 was benchmarked using an ADSL table at 20 row intervals between 1,000 and 1,000,000 with a minimum of 10 iterations carried out for each interval. The results are as follows.

Min and Median benchmark timesMin and Median benchmark times

Min and Median benchmark times

AET01

AET01 was benchmarked using an ADAEtable at 20 row intervals between 1,000 and 746,082 with a minimum of 10 iterations carried out for each interval. The ADSL table used for totals contained 20,000 rows. The results are as follows.

Min and Median benchmark timesMin and Median benchmark times

Min and Median benchmark times

Example

This example shows you how to create a demography table:

Carrying out data prep from TLG catalog DMT01

library(dtlg)
#Recreating data prep from TLG Catalog DMT01
adsl <- random.cdisc.data::cadsl
adsl <- adsl|>
  dplyr::mutate(adsl,
    SEX = factor(dplyr::case_when(
      SEX == "M" ~ "Male",
      SEX == "F" ~ "Female",
      SEX == "U" ~ "Unknown",
      SEX == "UNDIFFERENTIATED" ~ "Undifferentiated"
    )),
    AGEGR1 = factor(
      dplyr::case_when(
        dplyr::between(AGE, 18, 40) ~ "18-40",
        dplyr::between(AGE, 41, 64) ~ "41-64",
        AGE > 64 ~ ">=65"
      ),
      levels = c("18-40", "41-64", ">=65")
    ))

Creating DMT01

vars <- c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "BMRKR1")
var_labels <- c(
  "Age (yr)",
  "Age Group",
  "Sex",
  "Ethnicity",
  "Race",
  "Continous Level Biomarker 1"
)
DMT01<-summary_table(adsl, target = vars, treat = 'ARM', target_name = var_labels)
stats A: Drug X C: Combination B: Placebo
Age (yr)
    n 134 132 134
    Mean (SD) 33.77 (6.55) 35.43 (7.72) 35.43 (7.9)
    Median 33 35 35
    Min, Max 21, 50 20, 69 21, 62
    Missing 0 0 0
Age Group
    18-40 113 106 103
    41-64 21 25 31
    \>=65 0 1 0
Sex
    Female 79 70 82
    Male 55 62 52
Ethnicity
    HISPANIC OR LATINO 15 15 18
    NOT HISPANIC OR LATINO 104 101 103
    NOT REPORTED 6 11 10
    UNKNOWN 9 5 3
Race
    ASIAN 68 73 67
    BLACK OR AFRICAN AMERICAN 31 32 28
    WHITE 27 21 26
    AMERICAN INDIAN OR ALASKA NATIVE 8 6 11
    MULTIPLE 0 0 1
    NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 0 1
Continous Level Biomarker 1
    n 134 132 134
    Mean (SD) 5.97 (3.55) 5.62 (3.49) 5.7 (3.31)
    Median 5.39 4.61 4.81
    Min, Max 0.41, 17.67 0.17, 21.39 0.65, 14.24
    Missing 0 0 0

This example shows how to create a labs table like LBT01

Change from baseline is not currently available

adlb <- random.cdisc.data::cadlb|>dplyr::filter(AVISIT != "SCREENING")
labs <- summary_table_by_targets(adlb, c('AVAL','CHG'), 'ARM', c('PARAM','AVISIT'))
stats A: Drug X.AVAL A: Drug X.CHG C: Combination.AVAL C: Combination.CHG B: Placebo.AVAL B: Placebo.CHG
Alanine Aminotransferase Measurement NA NA NA NA NA NA
    BASELINE
        n 134 134 132 132 134 134
        Mean (SD) 17.74 (9.93) 0 (0) 19.46 (9.08) 0 (0) 18.71 (9.83) 0 (0)
        Median 17.46 0 18.97 0 18.19 0
        Min, Max 0, 44.06 0, 0 0.57, 39.81 0, 0 1.48, 54.4 0, 0
        Missing 0 0 0 0 0 0
    WEEK 1 DAY 8
        n 134 134 132 132 134 134
        Mean (SD) 16.75 (9.08) -0.99 (13.49) 19.61 (9.27) 0.14 (12.85) 18.93 (9.18) 0.23 (14.26)
        Median 16.02 -1.28 19 0.06 18.22 -1.54
        Min, Max 0.05, 36.3 -31.31, 27.89 0.91, 44.75 -32.45, 38.85 0.66, 39.89 -30.72, 33.92
        Missing 0 0 0 0 0 0
    WEEK 2 DAY 15
        n 134 134 132 132 134 134
        Mean (SD) 17.82 (9.6) 0.08 (14.15) 16.55 (8.15) -2.92 (12.64) 18.82 (9.73) 0.11 (14.45)
        Median 15.92 0.28 17.02 -1.11 17.96 -0.93
        Min, Max 0.4, 44.33 -32.89, 40.55 0.35, 34.69 -28.36, 23.98 0.18, 44.34 -45.93, 29.85
        Missing 0 0 0 0 0 0
    WEEK 3 DAY 22
        n 134 134 132 132 134 134
        Mean (SD) 18.37 (9.3) 0.63 (13.85) 16.75 (9.54) -2.71 (13.22) 17.65 (9.58) -1.06 (13.58)
        Median 18.11 1.13 15.1 -2.66 17.68 -0.49
        Min, Max 0.59, 41.73 -40.09, 31.24 0.48, 39.23 -30.63, 26.51 0.02, 38.61 -46.3, 31.38
        Missing 0 0 0 0 0 0
    WEEK 4 DAY 29
        n 134 134 132 132 134 134
        Mean (SD) 19.17 (10.95) 1.44 (15.39) 17.92 (9.32) -1.54 (12.63) 17.22 (10.64) -1.48 (15.2)
        Median 17.41 0.88 17.71 -1.53 15.88 -2.96
        Min, Max 0.93, 54.24 -32.93, 46.98 0.25, 41.27 -30.33, 27.99 0.39, 47.96 -41.45, 43.08
        Missing 0 0 0 0 0 0
    WEEK 5 DAY 36
        n 134 134 132 132 134 134
        Mean (SD) 19.22 (9.47) 1.48 (14.49) 18.51 (9.43) -0.95 (12.92) 18.01 (9.92) -0.69 (14.65)
        Median 19.8 2.31 19.26 -2.48 18.28 1.82
        Min, Max 0.01, 43.42 -40.08, 30.07 0.02, 37.46 -29.78, 25 0.11, 40.64 -47.6, 26.04
        Missing 0 0 0 0 0 0
C-Reactive Protein Measurement NA NA NA NA NA NA
    BASELINE
        n 134 134 132 132 134 134
        Mean (SD) 9.06 (0.93) 0 (0) 8.98 (0.89) 0 (0) 8.99 (0.98) 0 (0)
        Median 9.07 0 8.96 0 8.92 0
        Min, Max 6.21, 11.87 0, 0 6.24, 11.18 0, 0 6.23, 11.63 0, 0
        Missing 0 0 0 0 0 0
    WEEK 1 DAY 8
        n 134 134 132 132 134 134
        Mean (SD) 9.01 (1.03) -0.05 (1.38) 8.96 (1.03) -0.02 (1.3) 8.98 (1) 0 (1.43)
        Median 8.93 -0.17 8.91 0.02 8.96 -0.07
        Min, Max 6.37, 11.33 -3.56, 3.48 7.15, 12.15 -3.28, 3.33 6.84, 11.53 -3.14, 4.28
        Missing 0 0 0 0 0 0
    WEEK 2 DAY 15
        n 134 134 132 132 134 134
        Mean (SD) 8.87 (1.04) -0.19 (1.47) 9.12 (0.96) 0.15 (1.25) 8.99 (1.05) 0.01 (1.45)
        Median 8.86 -0.27 9.17 0.15 8.99 0
        Min, Max 6.78, 12.55 -4.53, 4.45 6.68, 11.54 -2.92, 3.28 6.55, 12.73 -3.79, 3.43
        Missing 0 0 0 0 0 0
    WEEK 3 DAY 22
        n 134 134 132 132 134 134
        Mean (SD) 9.08 (0.96) 0.03 (1.38) 8.99 (0.93) 0.02 (1.34) 8.97 (1.07) -0.02 (1.49)
        Median 9.09 0.15 8.97 0.2 8.97 -0.04
        Min, Max 6.46, 11.6 -3.95, 2.99 6.3, 11.33 -2.76, 3.26 5.81, 11.37 -4.28, 4.24
        Missing 0 0 0 0 0 0
    WEEK 4 DAY 29
        n 134 134 132 132 134 134
        Mean (SD) 8.8 (1.06) -0.26 (1.45) 8.97 (0.95) -0.01 (1.17) 9.04 (0.9) 0.05 (1.24)
        Median 8.71 -0.37 8.88 -0.06 9.04 0.1
        Min, Max 5.87, 12.22 -3.74, 4.15 7.16, 11.96 -3.06, 3.22 6.42, 11.21 -3.34, 3.71
        Missing 0 0 0 0 0 0
    WEEK 5 DAY 36
        n 134 134 132 132 134 134
        Mean (SD) 9.04 (1.04) -0.02 (1.5) 9.01 (0.91) 0.03 (1.27) 9.06 (1.01) 0.07 (1.34)
        Median 8.98 0.01 8.96 0.05 9.1 0.15
        Min, Max 6.92, 12.12 -4.15, 3.96 6.64, 11.93 -3.63, 4.78 7.08, 12.5 -3.5, 3.53
        Missing 0 0 0 0 0 0
Immunoglobulin A Measurement NA NA NA NA NA NA
    BASELINE
        n 134 134 132 132 134 134
        Mean (SD) 2.89 (0.08) 0 (0) 2.9 (0.11) 0 (0) 2.88 (0.1) 0 (0)
        Median 2.89 0 2.91 0 2.89 0
        Min, Max 2.7, 3.12 0, 0 2.61, 3.15 0, 0 2.64, 3.14 0, 0
        Missing 0 0 0 0 0 0
    WEEK 1 DAY 8
        n 134 134 132 132 134 134
        Mean (SD) 2.91 (0.1) 0.02 (0.14) 2.91 (0.09) 0.01 (0.14) 2.91 (0.1) 0.03 (0.15)
        Median 2.91 0.02 2.92 0.01 2.91 0.04
        Min, Max 2.66, 3.21 -0.32, 0.34 2.71, 3.18 -0.34, 0.32 2.69, 3.16 -0.28, 0.38
        Missing 0 0 0 0 0 0
    WEEK 2 DAY 15
        n 134 134 132 132 134 134
        Mean (SD) 2.91 (0.09) 0.02 (0.13) 2.89 (0.09) -0.01 (0.14) 2.9 (0.09) 0.02 (0.14)
        Median 2.91 0.03 2.91 -0.01 2.91 0.02
        Min, Max 2.67, 3.22 -0.34, 0.32 2.7, 3.15 -0.4, 0.35 2.62, 3.15 -0.42, 0.36
        Missing 0 0 0 0 0 0
    WEEK 3 DAY 22
        n 134 134 132 132 134 134
        Mean (SD) 2.89 (0.1) 0 (0.13) 2.9 (0.11) 0 (0.16) 2.9 (0.09) 0.02 (0.13)
        Median 2.89 0 2.9 0 2.91 0.03
        Min, Max 2.65, 3.15 -0.28, 0.33 2.65, 3.26 -0.38, 0.5 2.6, 3.2 -0.46, 0.38
        Missing 0 0 0 0 0 0
    WEEK 4 DAY 29
        n 134 134 132 132 134 134
        Mean (SD) 2.92 (0.1) 0.03 (0.13) 2.9 (0.11) -0.01 (0.15) 2.91 (0.1) 0.03 (0.14)
        Median 2.93 0.03 2.89 -0.01 2.9 0.02
        Min, Max 2.63, 3.16 -0.39, 0.37 2.59, 3.19 -0.38, 0.37 2.58, 3.15 -0.3, 0.42
        Missing 0 0 0 0 0 0
    WEEK 5 DAY 36
        n 134 134 132 132 134 134
        Mean (SD) 2.91 (0.09) 0.02 (0.12) 2.9 (0.1) -0.01 (0.16) 2.9 (0.1) 0.02 (0.14)
        Median 2.91 0.02 2.91 0 2.92 0.02
        Min, Max 2.6, 3.08 -0.33, 0.28 2.61, 3.13 -0.52, 0.34 2.62, 3.13 -0.3, 0.35
        Missing 0 0 0 0 0 0

This example shows how to create AET01

Follow example in merge_table_lists to get the relevant tables created. This mirrors the set up in the TLGcatalog.

a<-event_count(adsl,patient = "USUBJID",treat = "ARM", label = "Total number of deaths", .filters = "DTHFL == 'Y'", .total_dt = NULL)
b<-event_count(adsl,patient = "USUBJID",treat = "ARM", label = "Total number of patients withdrawn from study due to an AE", .filters = "DCSREAS == 'ADVERSE EVENT'", .total_dt = NULL)
d<-event_count(adae, patient = "USUBJID", treat = "ARM", label = "Total number of patients with at least one AE",.total_dt = adsl)

e <- total_events(adae, 'ARM', 'Total number of AEs')
aesi_vars <- c("FATAL", "SER", "SERWD", "SERDSM", "RELSER", "WD", "DSM", "REL", "RELWD", "RELDSM", "SEV")
f <- multi_event_true(adae, event_vars = aesi_vars, patient = "USUBJID", treat = "ARM", heading = "Total number of patients with at least one", .total_dt = adsl, indent = "  ")

AET01 <- merge_table_lists(list(d,e,a,b,f))
stats A: Drug X C: Combination B: Placebo
Total number of patients with at least one AE 100 (74.63%) 103 (78.03%) 98 (73.13%)
Total number of AEs 502 604 480
Total number of deaths 25 (18.66%) 22 (16.67%) 23 (17.16%)
Total number of patients withdrawn from study due to an AE 3 (2.24%) 5 (3.79%) 6 (4.48%)
Total number of patients with at least one
AE with fatal outcome 5 (3.73%) 6 (4.55%) 5 (3.73%)
Serious AE 85 (63.43%) 87 (65.91%) 80 (59.7%)
Serious AE leading to withdrawal from treatment 6 (4.48%) 9 (6.82%) 12 (8.96%)
Serious AE leading to dose modification/interruption 36 (26.87%) 47 (35.61%) 40 (29.85%)
Related Serious AE 64 (47.76%) 64 (48.48%) 52 (38.81%)
AE leading to withdrawal from treatment 20 (14.93%) 26 (19.7%) 24 (17.91%)
AE leading to dose modification/interruption 63 (47.01%) 77 (58.33%) 70 (52.24%)
Related AE 86 (64.18%) 92 (69.7%) 85 (63.43%)
Related AE leading to withdrawal from treatment 10 (7.46%) 12 (9.09%) 9 (6.72%)
Related AE leading to dose modification/interruption 44 (32.84%) 51 (38.64%) 44 (32.84%)
Severe AE (at greatest intensity) 77 (57.46%) 79 (59.85%) 70 (52.24%)

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An R package repository for pharma tables, listings and graphs developed using data table

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