The baseline characteristic analysis aims to provide tables to summarize details of participants. The development of baseline characteristic analysis involves functions:
prepare_base_char: prepare analysis raw datasets.format_base_char: prepare analysis outdata with proper
format.rtf_base_char: transfer output dataset to RTF
table.adsl <- r2rtf::r2rtf_adsl
adsl$TRTA <- adsl$TRT01A
adsl$TRTA <- factor(
adsl$TRTA,
levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"),
labels = c("Placebo", "Low Dose", "High Dose")
)meta <- meta_sl(
dataset_population = adsl,
population_term = "apat",
parameter_term = "age;gender;race",
parameter_var = "AGE^AGEGR1;SEX;RACE",
treatment_group = "TRTA"
)## ADaM metadata:
## .$data_population Population data with 254 subjects
## .$data_observation Observation data with 254 records
## .$plan Analysis plan with 1 plans
##
##
## Analysis population type:
## name id group
## 1 'apat' 'USUBJID' 'TRTA'
## var
## 1 STUDYID, USUBJID, SUBJID, SITEID, SITEGR1, ARM, TRT01P, TRT01PN, TRT01A, TRT01AN, TRTSDT, TRTEDT, TRTDUR, AVGDD, CUMDOSE, AGE, AGEGR1, AGEGR1N, AGEU, RACE, RACEN, SEX, ETHNIC, SAFFL, ITTFL, EFFFL, COMP8FL, COMP16FL, COMP24FL, DISCONFL, DSRAEFL, DTHFL, BMIBL, BMIBLGR1, HEIGHTBL, WEIGHTBL, EDUCLVL, DISONSDT, DURDIS, DURDSGR1, VISIT1DT, RFSTDTC, RFENDTC, VISNUMEN, RFENDT, DCDECOD, DCREASCD, MMSETOT, TRTA
## subset label
## 1 SAFFL == 'Y' 'All Participants as Treated'
##
##
## Analysis observation type:
## name id group
## 1 'apat' 'USUBJID' 'TRTA'
## var
## 1 STUDYID, USUBJID, SUBJID, SITEID, SITEGR1, ARM, TRT01P, TRT01PN, TRT01A, TRT01AN, TRTSDT, TRTEDT, TRTDUR, AVGDD, CUMDOSE, AGE, AGEGR1, AGEGR1N, AGEU, RACE, RACEN, SEX, ETHNIC, SAFFL, ITTFL, EFFFL, COMP8FL, COMP16FL, COMP24FL, DISCONFL, DSRAEFL, DTHFL, BMIBL, BMIBLGR1, HEIGHTBL, WEIGHTBL, EDUCLVL, DISONSDT, DURDIS, DURDSGR1, VISIT1DT, RFSTDTC, RFENDTC, VISNUMEN, RFENDT, DCDECOD, DCREASCD, MMSETOT, TRTA
## subset label
## 1 SAFFL == 'Y' ''
##
##
## Analysis parameter type:
## name label subset
## 1 'age' 'Age'
## 2 'gender' 'Sex'
## 3 'race' 'Race'
##
##
## Analysis function:
## name label
## 1 'base_char' ''The input of the function prepare_base_char() is a
meta object created by the metalite package.
## List of 14
## $ meta :List of 7
## $ population : chr "apat"
## $ observation : chr "apat"
## $ parameter : chr "age;gender;race"
## $ n :'data.frame': 1 obs. of 6 variables:
## $ order : NULL
## $ group : chr "TRTA"
## $ reference_group: NULL
## $ char_n :List of 3
## $ char_var : chr [1:3] "AGE" "SEX" "RACE"
## $ char_prop :List of 3
## $ var_type :List of 3
## $ group_label : Factor w/ 3 levels "Placebo","Low Dose",..: 1 3 2
## $ analysis : chr "base_char"## name n_1 n_2 n_3 n_9999 var_label
## 1 Participants in population 86 84 84 254 -----## [[1]]
## name Placebo Low Dose High Dose Total var_label
## 1 65-80 42 47 55 144 Age
## 2 <65 14 8 11 33 Age
## 3 >80 30 29 18 77 Age
## 4 <NA> <NA> <NA> <NA> <NA> Age
## 5 Mean 75.2 75.7 74.4 75.1 Age
## 6 SD 8.6 8.3 7.9 8.2 Age
## 7 SE 0.9 0.9 0.9 0.5 Age
## 8 Median 76.0 77.5 76.0 77.0 Age
## 9 Min 52.0 51.0 56.0 51.0 Age
## 10 Max 89.0 88.0 88.0 89.0 Age
## 11 Q1 to Q3 69.25 to 81.75 71 to 82 70.75 to 80 70 to 81 Age
## 12 Range 52 to 89 51 to 88 56 to 88 51 to 89 Age
##
## [[2]]
## name Placebo Low Dose High Dose Total var_label
## 1 F 53 50 40 143 Sex
## 2 M 33 34 44 111 Sex
##
## [[3]]
## name Placebo Low Dose High Dose Total var_label
## 1 AMERICAN INDIAN OR ALASKA NATIVE 0 0 1 1 Race
## 2 BLACK OR AFRICAN AMERICAN 8 6 9 23 Race
## 3 WHITE 78 78 74 230 Race## [1] "AGE" "SEX" "RACE"## [[1]]
## name Placebo Low Dose High Dose Total var_label
## 1 65-80 48.83721 55.95238 65.47619 56.69291 Age
## 2 <65 16.27907 9.52381 13.09524 12.99213 Age
## 3 >80 34.88372 34.52381 21.42857 30.31496 Age
## 4 <NA> NA NA NA NA Age
## 5 Mean NA NA NA NA Age
## 6 SD NA NA NA NA Age
## 7 SE NA NA NA NA Age
## 8 Median NA NA NA NA Age
## 9 Min NA NA NA NA Age
## 10 Max NA NA NA NA Age
## 11 Q1 to Q3 NA NA NA NA Age
## 12 Range NA NA NA NA Age
##
## [[2]]
## name Placebo Low Dose High Dose Total var_label
## 1 F 61.62791 59.52381 47.61905 56.29921 Sex
## 2 M 38.37209 40.47619 52.38095 43.70079 Sex
##
## [[3]]
## name Placebo Low Dose High Dose Total
## 1 AMERICAN INDIAN OR ALASKA NATIVE 0.000000 0.000000 1.190476 0.3937008
## 2 BLACK OR AFRICAN AMERICAN 9.302326 7.142857 10.714286 9.0551181
## 3 WHITE 90.697674 92.857143 88.095238 90.5511811
## var_label
## 1 Race
## 2 Race
## 3 Raceformat_base_char to prepare analysis dataset before
generate RTF output
## name n_1 p_1 n_2 p_2
## 1 Participants in population 86 <NA> 84 <NA>
## 2 65-80 42 (48.84) 47 (55.95)
## 3 <65 14 (16.28) 8 (9.52)
## 4 >80 30 (34.88) 29 (34.52)
## 5 <NA> <NA> <NA> <NA> <NA>
## 6 Mean 75.2 <NA> 75.7 <NA>
## 7 SD 8.6 <NA> 8.3 <NA>
## 8 SE 0.9 <NA> 0.9 <NA>
## 9 Median 76.0 <NA> 77.5 <NA>
## 10 Q1 to Q3 69.25 to 81.75 <NA> 71 to 82 <NA>
## 11 Range 52 to 89 <NA> 51 to 88 <NA>
## 12 F 53 (61.63) 50 (59.52)
## 13 M 33 (38.37) 34 (40.48)
## 14 AMERICAN INDIAN OR ALASKA NATIVE 0 (0.00) 0 (0.00)
## 15 BLACK OR AFRICAN AMERICAN 8 (9.30) 6 (7.14)
## 16 WHITE 78 (90.70) 78 (92.86)
## n_3 p_3 n_9999 p_9999 var_label
## 1 84 <NA> 254 <NA> -----
## 2 55 (65.48) 144 (56.69) Age
## 3 11 (13.10) 33 (12.99) Age
## 4 18 (21.43) 77 (30.31) Age
## 5 <NA> <NA> <NA> <NA> Age
## 6 74.4 <NA> 75.1 <NA> Age
## 7 7.9 <NA> 8.2 <NA> Age
## 8 0.9 <NA> 0.5 <NA> Age
## 9 76.0 <NA> 77.0 <NA> Age
## 10 70.75 to 80 <NA> 70 to 81 <NA> Age
## 11 56 to 88 <NA> 51 to 89 <NA> Age
## 12 40 (47.62) 143 (56.30) Sex
## 13 44 (52.38) 111 (43.70) Sex
## 14 1 (1.19) 1 (0.39) Race
## 15 9 (10.71) 23 (9.06) Race
## 16 74 (88.10) 230 (90.55) Racertf_base_char to generate RTF output
outdata |> rtf_base_char(
source = "Source: [CDISCpilot: adam-adsl]",
path_outdata = tempfile(fileext = ".Rdata"),
path_outtable = "outtable/base0char.rtf"
)## The outdata is saved in/rtmp/RtmpkBEdeP/file3606c12a1d26cc.Rdata## The output is saved in/rtmp/Rtmp7YMja5/Rbuild3605e43666ac3c/metalite.sl/vignettes/outtable/base0char.rtfThe baseline characteristic subgroup analysis aims to provide tables to summarize details of participants by subgroup. The development of baseline characteristic subgroup analysis involves functions:
prepare_base_char_subgroup(): prepare analysis raw
datasets.format_base_char_subgroup(): prepare analysis outdata
with proper format.rtf_base_char_subgroup(): transfer output dataset to
RTF table.adsl <- r2rtf::r2rtf_adsl
adsl$TRTA <- adsl$TRT01A
adsl$TRTA <- factor(
adsl$TRTA,
levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"),
labels = c("Placebo", "Low Dose", "High Dose")
)plan <- plan(
analysis = "base_char_subgroup",
population = "apat",
observation = "apat",
parameter = "age;gender;race"
)meta <- meta_adam(
population = adsl,
observation = adsl
) |>
define_plan(plan = plan) |>
define_population(
name = "apat",
group = "TRTA",
subset = quote(SAFFL == "Y"),
var = c("USUBJID", "TRTA", "SAFFL", "AGEGR1", "SEX", "RACE")
) |>
define_parameter(
name = "age",
var = "AGE",
label = "Age (years)",
vargroup = "AGEGR1"
) |>
define_parameter(
name = "race",
var = "RACE",
label = "Race"
) |>
define_analysis(
name = "base_char_subgroup",
title = "Participant by Age Category and Sex",
label = "baseline characteristic sub group table"
) |>
meta_build()## Warning in FUN(X[[i]], ...): gender: has missing label## ADaM metadata:
## .$data_population Population data with 254 subjects
## .$data_observation Observation data with 254 records
## .$plan Analysis plan with 1 plans
##
##
## Analysis population type:
## name id group var subset
## 1 'apat' 'USUBJID' 'TRTA' USUBJID, TRTA, SAFFL, AGEGR1, SEX, RACE SAFFL == 'Y'
## label
## 1 'All Participants as Treated'
##
##
## Analysis observation type:
## name id group var subset label
## 1 'apat' 'USUBJID' 'TRTA' 'All Participants as Treated'
##
##
## Analysis parameter type:
## name label subset
## 1 'age' 'Age (years)'
## 2 'race' 'Race'
## 3 'gender'
##
##
## Analysis function:
## name label
## 1 'base_char_subgroup' 'baseline characteristic sub group table'The input of the function prepare_base_char_subgroup()
is a meta object created by the metalite package.
The output of the function is an outdata object
containing a list of analysis raw datasets. Key arguments are:
subgroup_var: a character value of subgroup variable
name in observation data saved in meta$data_observation.subgroup_header: a character vector for column header
hierarchy. The first element will be the first level header and the
second element will be second level header.outdata <- prepare_base_char_subgroup(
meta,
population = "apat",
parameter = "age;race",
subgroup_var = "TRTA",
subgroup_header = c("SEX", "TRTA"),
display_subgroup_total = TRUE
)The output dataset contains commonly used statistics within each
subgroup_var.
## List of 14
## $ meta :List of 7
## $ population : chr "apat"
## $ observation : chr "apat"
## $ parameter : chr "age;race"
## $ n :'data.frame': 1 obs. of 5 variables:
## $ order : NULL
## $ group : chr "SEX"
## $ reference_group: NULL
## $ char_n :List of 2
## $ char_var : chr [1:2] "AGE" "RACE"
## $ char_prop :List of 2
## $ var_type :List of 2
## $ group_label : Factor w/ 2 levels "F","M": 1 2
## $ analysis : chr "base_char_subgroup"## List of 14
## $ meta :List of 7
## $ population : chr "apat"
## $ observation : chr "apat"
## $ parameter : chr "age;race"
## $ n :'data.frame': 1 obs. of 5 variables:
## $ order : NULL
## $ group : chr "SEX"
## $ reference_group: NULL
## $ char_n :List of 2
## $ char_var : chr [1:2] "AGE" "RACE"
## $ char_prop :List of 2
## $ var_type :List of 2
## $ group_label : Factor w/ 2 levels "F","M": 2 1
## $ analysis : chr "base_char_subgroup"## List of 14
## $ meta :List of 7
## $ population : chr "apat"
## $ observation : chr "apat"
## $ parameter : chr "age;race"
## $ n :'data.frame': 1 obs. of 5 variables:
## $ order : NULL
## $ group : chr "SEX"
## $ reference_group: NULL
## $ char_n :List of 2
## $ char_var : chr [1:2] "AGE" "RACE"
## $ char_prop :List of 2
## $ var_type :List of 2
## $ group_label : Factor w/ 2 levels "F","M": 2 1
## $ analysis : chr "base_char_subgroup"The information about subgroup saved with outdata$group
and outdata$subgroup.
## [1] "F" "M"## [1] "Placebo" "Low Dose" "High Dose"n_pop: participants in population within each
subgroup_var.
## name n_1 n_2 n_9999 var_label
## 1 Participants in population 53 33 86 -----## name n_1 n_2 n_9999 var_label
## 1 Participants in population 40 44 84 -----## name n_1 n_2 n_9999 var_label
## 1 Participants in population 50 34 84 -----format_base_char_subgroup to prepare analysis dataset
before generate RTF output
## name var_label order Placebon_1 Placebop_1
## 9 Participants in population ----- 1 53 <NA>
## 1 65-80 Age (years) 2 22 (41.5)
## 2 <65 Age (years) 3 9 (17.0)
## 3 >80 Age (years) 4 22 (41.5)
## 14 <NA> Age (years) 5 <NA> <NA>
## 7 Mean Age (years) 6 76.4 <NA>
## 11 SD Age (years) 7 8.7 <NA>
## 8 Median Age (years) 8 78.0 <NA>
## 10 Range Age (years) 9 59 to 89 <NA>
## 4 AMERICAN INDIAN OR ALASKA NATIVE Race 10 <NA> <NA>
## 5 BLACK OR AFRICAN AMERICAN Race 10 5 (9.4)
## 6 BLACK OR AFRICAN AMERICAN Race 11 <NA> <NA>
## 12 WHITE Race 11 48 (90.6)
## 13 WHITE Race 12 <NA> <NA>
## Placebon_2 Placebop_2 Placebon_9999 Placebop_9999 Low Dosen_1 Low Dosep_1
## 9 33 <NA> 86 <NA> 50 <NA>
## 1 20 (60.6) 42 (48.8) 28 (56.0)
## 2 5 (15.2) 14 (16.3) 5 (10.0)
## 3 8 (24.2) 30 (34.9) 17 (34.0)
## 14 <NA> <NA> <NA> <NA> <NA> <NA>
## 7 73.4 <NA> 75.2 <NA> 75.7 <NA>
## 11 8.1 <NA> 8.6 <NA> 8.1 <NA>
## 8 74.0 <NA> 76.0 <NA> 77.5 <NA>
## 10 52 to 85 <NA> 52 to 89 <NA> 54 to 87 <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA>
## 5 3 (9.1) 8 (9.3) 6 (12.0)
## 6 <NA> <NA> <NA> <NA> <NA> <NA>
## 12 30 (90.9) 78 (90.7) 44 (88.0)
## 13 <NA> <NA> <NA> <NA> <NA> <NA>
## Low Dosen_2 Low Dosep_2 Low Dosen_9999 Low Dosep_9999 High Dosen_1
## 9 34 <NA> 84 <NA> 40
## 1 19 (55.9) 47 (56.0) 28
## 2 3 (8.8) 8 (9.5) 5
## 3 12 (35.3) 29 (34.5) 7
## 14 <NA> <NA> <NA> <NA> <NA>
## 7 75.6 <NA> 75.7 <NA> 74.7
## 11 8.7 <NA> 8.3 <NA> 7.7
## 8 77.5 <NA> 77.5 <NA> 76.0
## 10 51 to 88 <NA> 51 to 88 <NA> 56 to 88
## 4 <NA> <NA> <NA> <NA> 0
## 5 0 (0.0) 6 (7.1) <NA>
## 6 <NA> <NA> <NA> <NA> 6
## 12 34 (100.0) 78 (92.9) <NA>
## 13 <NA> <NA> <NA> <NA> 34
## High Dosep_1 High Dosen_2 High Dosep_2 High Dosen_9999 High Dosep_9999
## 9 <NA> 44 <NA> 84 <NA>
## 1 (70.0) 27 (61.4) 55 (65.5)
## 2 (12.5) 6 (13.6) 11 (13.1)
## 3 (17.5) 11 (25.0) 18 (21.4)
## 14 <NA> <NA> <NA> <NA> <NA>
## 7 <NA> 74.1 <NA> 74.4 <NA>
## 11 <NA> 8.2 <NA> 7.9 <NA>
## 8 <NA> 77.0 <NA> 76.0 <NA>
## 10 <NA> 56 to 86 <NA> 56 to 88 <NA>
## 4 (0.0) 1 (2.3) 1 (1.2)
## 5 <NA> <NA> <NA> <NA> <NA>
## 6 (15.0) 3 (6.8) 9 (10.7)
## 12 <NA> <NA> <NA> <NA> <NA>
## 13 (85.0) 40 (90.9) 74 (88.1)rtf_base_char_subgroup to generate RTF output
outdata |>
rtf_base_char_subgroup(
source = "Source: [CDISCpilot: adam-adsl]",
path_outdata = tempfile(fileext = ".Rdata"),
path_outtable = "outtable/base0charsubgroup.rtf"
)## The outdata is saved in/rtmp/RtmpkBEdeP/file3606c118521865.Rdata## The output is saved in/rtmp/Rtmp7YMja5/Rbuild3605e43666ac3c/metalite.sl/vignettes/outtable/base0charsubgroup.rtf