Disposition Table
disposition.RmdCreate Disposition Table
The objective of this tutorial is to generate a production-ready Disposition table specification analyses.
This report produces a table that contains counts and percentage of disposition for the participants in population for overall study. To accomplish this using metalite.sl, four essential functions are required:
prepare_disposition ():subset data for disposition analysis.format_disposition(): prepare analysis outdata with proper format.rtf_disposition (): transfer output dataset to RTF table.
An example output:
Example data
Within metalite.sl, we utilized the ADSL datasets from the metalite package to create an illustrative dataset. The metadata structure remains consistent across all analysis examples within metalite.sl. Additional information can be accessed on the metalite package website.
Build a metadata
adsl <- metalite_sl_adsl
head(adsl)
#> USUBJID STUDYID SUBJID SITEID SITEGR1 ARM
#> 1 01-701-1015 CDISCPILOT01 1015 701 701 Placebo
#> 2 01-701-1023 CDISCPILOT01 1023 701 701 Placebo
#> 3 01-701-1028 CDISCPILOT01 1028 701 701 Xanomeline High Dose
#> 4 01-701-1033 CDISCPILOT01 1033 701 701 Xanomeline Low Dose
#> 5 01-701-1034 CDISCPILOT01 1034 701 701 Xanomeline High Dose
#> 6 01-701-1047 CDISCPILOT01 1047 701 701 Placebo
#> TRT01P TRT01PN TRT01A TRT01AN TRTSDT
#> 1 Placebo 0 Placebo 0 2014-01-02
#> 2 Placebo 0 Placebo 0 2012-08-05
#> 3 Xanomeline High Dose 81 Xanomeline High Dose 81 2013-07-19
#> 4 Xanomeline Low Dose 54 Xanomeline Low Dose 54 2014-03-18
#> 5 Xanomeline High Dose 81 Xanomeline High Dose 81 2014-07-01
#> 6 Placebo 0 Placebo 0 2013-02-12
#> TRTEDT TRTDUR AVGDD CUMDOSE AGE AGEGR1 AGEGR1N AGEU RACE RACEN SEX
#> 1 2014-07-02 182 0.0 0 63 <65 1 YEARS WHITE 1 F
#> 2 2012-09-01 28 0.0 0 64 <65 1 YEARS WHITE 1 M
#> 3 2014-01-14 180 77.7 13986 71 65-80 2 YEARS WHITE 1 M
#> 4 2014-03-31 14 54.0 756 74 65-80 2 YEARS WHITE 1 M
#> 5 2014-12-30 183 76.9 14067 77 65-80 2 YEARS WHITE 1 F
#> 6 2013-03-09 26 0.0 0 85 >80 3 YEARS WHITE 1 F
#> ETHNIC SAFFL ITTFL EFFFL COMP8FL COMP16FL COMP24FL DISCONFL
#> 1 HISPANIC OR LATINO Y Y Y Y Y Y
#> 2 HISPANIC OR LATINO Y Y Y N N N Y
#> 3 NOT HISPANIC OR LATINO Y Y Y Y Y Y
#> 4 NOT HISPANIC OR LATINO Y Y Y N N N Y
#> 5 NOT HISPANIC OR LATINO Y Y Y Y Y Y
#> 6 NOT HISPANIC OR LATINO Y Y Y N N N Y
#> DSRAEFL DTHFL BMIBL BMIBLGR1 HEIGHTBL WEIGHTBL EDUCLVL DISONSDT DURDIS
#> 1 25.1 25-<30 147.3 54.4 16 2010-04-30 43.9
#> 2 Y 30.4 >=30 162.6 80.3 14 2006-03-11 76.4
#> 3 31.4 >=30 177.8 99.3 16 2009-12-16 42.8
#> 4 28.8 25-<30 175.3 88.5 12 2009-08-02 55.3
#> 5 26.1 25-<30 154.9 62.6 9 2011-09-29 32.9
#> 6 Y 30.4 >=30 148.6 67.1 8 2009-07-26 42.0
#> DURDSGR1 VISIT1DT RFSTDTC RFENDTC VISNUMEN RFENDT
#> 1 >=12 2013-12-26 2014-01-02 2014-07-02 12 2014-07-02
#> 2 >=12 2012-07-22 2012-08-05 2012-09-02 5 2012-09-02
#> 3 >=12 2013-07-11 2013-07-19 2014-01-14 12 2014-01-14
#> 4 >=12 2014-03-10 2014-03-18 2014-04-14 5 2014-04-14
#> 5 >=12 2014-06-24 2014-07-01 2014-12-30 12 2014-12-30
#> 6 >=12 2013-01-22 2013-02-12 2013-03-29 6 2013-03-29
#> DCDECOD DCREASCD MMSETOT TRTA AEACN
#> 1 COMPLETED Completed 23 Placebo <NA>
#> 2 ADVERSE EVENT Adverse Event 23 Placebo <NA>
#> 3 COMPLETED Completed 23 High Dose DRUG WITHDRAWN
#> 4 STUDY TERMINATED BY SPONSOR Sponsor Decision 23 Low Dose <NA>
#> 5 COMPLETED Completed 21 High Dose <NA>
#> 6 ADVERSE EVENT Adverse Event 23 Placebo DRUG WITHDRAWN
#> EOTSTT DCTREAS EOSSTT DCSREAS
#> 1 Complete <NA> Complete <NA>
#> 2 Complete <NA> Complete <NA>
#> 3 Discontinued Adverse Event Discontinued Other
#> 4 Complete <NA> Complete <NA>
#> 5 Discontinued Lost to Follow-Up Discontinued Lost to Follow-Up
#> 6 Discontinued Adverse Event Discontinued Other
plan <- plan(
analysis = "disp", population = "apat",
observation = "apat", parameter = "disposition;medical-disposition"
)
meta <- meta_adam(
population = adsl,
observation = adsl
) |>
define_plan(plan = plan) |>
define_population(
name = "apat",
group = "TRTA",
subset = quote(SAFFL == "Y")
) |>
define_parameter(
name = "disposition",
var = "EOSSTT",
label = "Trial Disposition",
var_lower = "DCSREAS"
) |>
define_parameter(
name = "medical-disposition",
var = "EOTSTT",
label = "Participant Study Medication Disposition",
var_lower = "DCTREAS"
) |>
define_analysis(
name = "disp",
title = "Disposition of Participant",
label = "disposition table"
) |>
meta_build()Click to show the output
meta
#> ADaM metadata:
#> .$data_population Population data with 254 subjects
#> .$data_observation Observation data with 254 records
#> .$plan Analysis plan with 1 plans
#>
#>
#> Analysis population type:
#> name id group var subset label
#> 1 'apat' 'USUBJID' 'TRTA' SAFFL == 'Y' 'All Participants as Treated'
#>
#>
#> Analysis observation type:
#> name id group var subset label
#> 1 'apat' 'USUBJID' 'TRTA' 'All Participants as Treated'
#>
#>
#> Analysis parameter type:
#> name label subset
#> 1 'disposition' 'Trial Disposition'
#> 2 'medical-disposition' 'Participant Study Medication Disposition'
#>
#>
#> Analysis function:
#> name label
#> 1 'disp' 'disposition table'Analysis preparation
The function prepare_disposition() is written to prepare
data for subject disposition analysis.The function takes four
arguments:
Meta is metadata object created by metalite and it contains data from ADSL. Analysis, Population, and Parameter arguments are used to subset and process the meta data. They have default values, which rely on the meta data object.
The function assign default value Analysis to
prepare_disposition, Population to the population value
associated with the prepare_disposition analysis in meta
plan, and parameter to the parameter(s) associated with the
prepare_disposition analysis in meta$plan.
However, the user can also manually specify the analysis, population, and parameter values when calling the function, if they want to override the default values.
In the body of the function, it calls another function
prepare_sl_summary with the same meta, analysis,
population, and parameter arguments. prepare_sl_summary
takes the meta data, subsets it based on the analysis, population, and
parameter values, and then calculates and returns a summary of the
relevant data.
The result of prepare_sl_summary is then returned as the
result of prepare_disposition.
The resulting output of the function
prepare_disposition() comprises a collection of raw
datasets for analysis and reporting.
outdata <- prepare_disposition(meta)Click to show the output
outdata
#> List of 14
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "apat"
#> $ parameter : chr "disposition;medical-disposition"
#> $ n :'data.frame': 1 obs. of 6 variables:
#> $ order : NULL
#> $ group : chr "TRTA"
#> $ reference_group: NULL
#> $ char_n :List of 2
#> $ char_var : chr [1:2] "EOSSTT" "EOTSTT"
#> $ char_prop :List of 2
#> $ var_type :List of 2
#> $ group_label : Factor w/ 3 levels "Placebo","Low Dose",..: 1 3 2
#> $ analysis : chr "disp"-
parameter: parameter name
outdata$parameter
#> [1] "disposition;medical-disposition"-
n: number of participants in population
outdata$n
#> name n_1 n_2 n_3 n_9999 var_label
#> 1 Participants in population 86 84 84 254 -----The resulting dataset contains frequently used statistics, with
variables indexed according to the order specified in
outdata$group.
outdata$group
#> [1] "TRTA"-
char_n: number of participants completed vs not completed in each parameter
outdata$char_n
#> [[1]]
#> name Placebo Low Dose High Dose Total var_label
#> 1 Complete 47 34 38 119 Trial Disposition
#> 2 Discontinued 26 37 38 101 Trial Disposition
#> 3 Lack of Efficacy 2 3 9 14 Trial Disposition
#> 4 Lost to Follow-Up 2 7 8 17 Trial Disposition
#> 5 Other 15 18 18 51 Trial Disposition
#> 6 Withdrawal By Subject 7 9 3 19 Trial Disposition
#> 7 Participants Ongoing 13 13 8 34 Trial Disposition
#>
#> [[2]]
#> name Placebo Low Dose High Dose Total
#> 1 Complete 47 34 38 119
#> 2 Discontinued 26 37 38 101
#> 3 Adverse Event 15 18 18 51
#> 4 Lack of Efficacy 2 3 9 14
#> 5 Lost to Follow-Up 2 7 8 17
#> 6 Withdrawal By Subject 7 9 3 19
#> 7 Participants Ongoing 13 13 8 34
#> var_label
#> 1 Participant Study Medication Disposition
#> 2 Participant Study Medication Disposition
#> 3 Participant Study Medication Disposition
#> 4 Participant Study Medication Disposition
#> 5 Participant Study Medication Disposition
#> 6 Participant Study Medication Disposition
#> 7 Participant Study Medication Disposition-
char_var: name of parameter
outdata$char_var
#> [1] "EOSSTT" "EOTSTT"-
char_prop: proportion of subject with disposition
outdata$char_prop
#> [[1]]
#> name Placebo Low Dose High Dose
#> 1 Complete 54.65116 40.47619 45.23810
#> 2 Discontinued 30.23256 44.04762 45.23810
#> 3 Lack of Efficacy 2.32558139534884 3.57142857142857 10.7142857142857
#> 4 Lost to Follow-Up 2.32558139534884 8.33333333333333 9.52380952380952
#> 5 Other 17.4418604651163 21.4285714285714 21.4285714285714
#> 6 Withdrawal By Subject 8.13953488372093 10.7142857142857 3.57142857142857
#> 7 Participants Ongoing 15.11628 15.47619 9.52381
#> Total var_label
#> 1 46.85039 Trial Disposition
#> 2 39.76378 Trial Disposition
#> 3 5.51181102362205 Trial Disposition
#> 4 6.69291338582677 Trial Disposition
#> 5 20.0787401574803 Trial Disposition
#> 6 7.48031496062992 Trial Disposition
#> 7 13.38583 Trial Disposition
#>
#> [[2]]
#> name Placebo Low Dose High Dose
#> 1 Complete 54.65116 40.47619 45.23810
#> 2 Discontinued 30.23256 44.04762 45.23810
#> 3 Adverse Event 17.4418604651163 21.4285714285714 21.4285714285714
#> 4 Lack of Efficacy 2.32558139534884 3.57142857142857 10.7142857142857
#> 5 Lost to Follow-Up 2.32558139534884 8.33333333333333 9.52380952380952
#> 6 Withdrawal By Subject 8.13953488372093 10.7142857142857 3.57142857142857
#> 7 Participants Ongoing 15.11628 15.47619 9.52381
#> Total var_label
#> 1 46.85039 Participant Study Medication Disposition
#> 2 39.76378 Participant Study Medication Disposition
#> 3 20.0787401574803 Participant Study Medication Disposition
#> 4 5.51181102362205 Participant Study Medication Disposition
#> 5 6.69291338582677 Participant Study Medication Disposition
#> 6 7.48031496062992 Participant Study Medication Disposition
#> 7 13.38583 Participant Study Medication DispositionFormat output
Once the raw analysis results are obtained, the
format_disposition() function can be employed to prepare
the outdata,ensuring its compatibility with production-ready RTF
tables.
outdata <- outdata |> format_disposition()Click to show the output
outdata$tbl
#> name n_1 p_1 n_2 p_2 n_3 p_3 n_9999 p_9999
#> 1 Participants in population 86 <NA> 84 <NA> 84 <NA> 254 <NA>
#> 2 Complete 47 (54.7) 34 (40.5) 38 (45.2) 119 (46.9)
#> 3 Discontinued 26 (30.2) 37 (44.0) 38 (45.2) 101 (39.8)
#> 4 Lack of Efficacy 2 (2.3) 3 (3.6) 9 (10.7) 14 (5.5)
#> 5 Lost to Follow-Up 2 (2.3) 7 (8.3) 8 (9.5) 17 (6.7)
#> 6 Other 15 (17.4) 18 (21.4) 18 (21.4) 51 (20.1)
#> 7 Withdrawal By Subject 7 (8.1) 9 (10.7) 3 (3.6) 19 (7.5)
#> 8 Participants Ongoing 13 (15.1) 13 (15.5) 8 (9.5) 34 (13.4)
#> 9 Complete 47 (54.7) 34 (40.5) 38 (45.2) 119 (46.9)
#> 10 Discontinued 26 (30.2) 37 (44.0) 38 (45.2) 101 (39.8)
#> 11 Adverse Event 15 (17.4) 18 (21.4) 18 (21.4) 51 (20.1)
#> 12 Lack of Efficacy 2 (2.3) 3 (3.6) 9 (10.7) 14 (5.5)
#> 13 Lost to Follow-Up 2 (2.3) 7 (8.3) 8 (9.5) 17 (6.7)
#> 14 Withdrawal By Subject 7 (8.1) 9 (10.7) 3 (3.6) 19 (7.5)
#> 15 Participants Ongoing 13 (15.1) 13 (15.5) 8 (9.5) 34 (13.4)
#> var_label
#> 1 -----
#> 2 Trial Disposition
#> 3 Trial Disposition
#> 4 Trial Disposition
#> 5 Trial Disposition
#> 6 Trial Disposition
#> 7 Trial Disposition
#> 8 Trial Disposition
#> 9 Participant Study Medication Disposition
#> 10 Participant Study Medication Disposition
#> 11 Participant Study Medication Disposition
#> 12 Participant Study Medication Disposition
#> 13 Participant Study Medication Disposition
#> 14 Participant Study Medication Disposition
#> 15 Participant Study Medication DispositionRTF tables
The last step is to prepare the RTF table using
rtf_trt_compliance.
outdata$tbl <- outdata$tbl %>%
mutate(name = ifelse(trimws(name) == "Status Not Recorded", " Status Not Recorded", name))
outdata |>
rtf_disposition(
"Source: [CDISCpilot: adam-adsl]",
path_outdata = tempfile(fileext = ".Rdata"),
path_outtable = "outtable/disposition.rtf"
)
#> The outdata is saved in/tmp/RtmpIUx4e5/file1ae92335b75a.Rdata
#> The output is saved in/home/runner/work/metalite.sl/metalite.sl/vignettes/outtable/disposition.rtf