Disposition Table
disposition.Rmd
library(metalite.sl)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
#> object 'type_sum.accel' not found
library(metalite)
library(dplyr)Create 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 <- 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")
)
# Create a variable EOSSTT indicating the end of end of study status
adsl$EOSSTT <- sample(
x = c("Participants Ongoing", "Discontinued"),
size = length(adsl$USUBJID),
prob = c(0.8, 0.2), replace = TRUE
)
adsl[adsl[["EOSSTT"]] == "Discontinued", "DCSREAS"] <- sample(
x = c("Adverse Event", "I/E Not Met", "Withdrew Consent", "Lack of Efficacy"),
size = length(adsl[adsl[["EOSSTT"]] == "Discontinued", "USUBJID"]),
prob = c(0.7, 0.1, 0.1, 0.1), replace = TRUE
)
# Create a variable EOTSTT1 indicating the end of treatment status part 1
adsl$EOTSTT1 <- ifelse(adsl$DISCONFL == "Y", "Discontinued", "Completed")
adsl$DCTREAS <- ifelse(adsl$EOTSTT1 == "Discontinued", adsl$DCREASCD, NA)
head(adsl)
#> STUDYID USUBJID SUBJID SITEID SITEGR1 ARM
#> 1 CDISCPILOT01 01-701-1015 1015 701 701 Placebo
#> 2 CDISCPILOT01 01-701-1023 1023 701 701 Placebo
#> 3 CDISCPILOT01 01-701-1028 1028 701 701 Xanomeline High Dose
#> 4 CDISCPILOT01 01-701-1033 1033 701 701 Xanomeline Low Dose
#> 5 CDISCPILOT01 01-701-1034 1034 701 701 Xanomeline High Dose
#> 6 CDISCPILOT01 01-701-1047 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
#> 1 COMPLETED Completed 23 Placebo
#> 2 ADVERSE EVENT Adverse Event 23 Placebo
#> 3 COMPLETED Completed 23 High Dose
#> 4 STUDY TERMINATED BY SPONSOR Sponsor Decision 23 Low Dose
#> 5 COMPLETED Completed 21 High Dose
#> 6 ADVERSE EVENT Adverse Event 23 Placebo
#> EOSSTT DCSREAS EOTSTT1 DCTREAS
#> 1 Participants Ongoing <NA> Completed <NA>
#> 2 Discontinued Adverse Event Discontinued Adverse Event
#> 3 Participants Ongoing <NA> Completed <NA>
#> 4 Participants Ongoing <NA> Discontinued Sponsor Decision
#> 5 Participants Ongoing <NA> Completed <NA>
#> 6 Participants Ongoing <NA> Discontinued Adverse Event
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 = "EOTSTT1",
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" "EOTSTT1"
#> $ 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$char_n
#> [[1]]
#> name Placebo Low Dose High Dose Total var_label
#> 1 Discontinued 24 12 20 56 Trial Disposition
#> 2 Adverse Event 18 9 13 40 Trial Disposition
#> 3 I/E Not Met 3 0 4 7 Trial Disposition
#> 4 Lack of Efficacy 2 2 2 6 Trial Disposition
#> 5 Withdrew Consent 1 1 1 3 Trial Disposition
#> 6 Participants Ongoing 62 72 64 198 Trial Disposition
#>
#> [[2]]
#> name Placebo Low Dose High Dose Total
#> 1 Completed 58 25 27 110
#> 2 Discontinued 28 59 57 144
#> 3 Adverse Event 8 44 40 92
#> 4 Death 2 1 0 3
#> 5 I/E Not Met 1 0 2 3
#> 6 Lack of Efficacy 3 0 1 4
#> 7 Lost to Follow-up 1 1 0 2
#> 8 Physician Decision 1 0 2 3
#> 9 Protocol Violation 1 1 1 3
#> 10 Sponsor Decision 2 2 3 7
#> 11 Withdrew Consent 9 10 8 27
#> var_label
#> 1 Participant Study Medication Disposition
#> 2 Participant Study Medication Disposition
#> 3 Participant Study Medication Disposition
#> 4 Participant Study Medication Disposition
#> 5 Participant Study Medication Disposition
#> 6 Participant Study Medication Disposition
#> 7 Participant Study Medication Disposition
#> 8 Participant Study Medication Disposition
#> 9 Participant Study Medication Disposition
#> 10 Participant Study Medication Disposition
#> 11 Participant Study Medication Disposition-
char_var: name of parameter
outdata$char_var
#> [1] "EOSSTT" "EOTSTT1"-
char_prop: proportion of subject with disposition
outdata$char_prop
#> [[1]]
#> name Placebo Low Dose High Dose
#> 1 Discontinued 27.90698 14.28571 23.80952
#> 2 Adverse Event 20.9302325581395 10.7142857142857 15.4761904761905
#> 3 I/E Not Met 3.48837209302326 0 4.76190476190476
#> 4 Lack of Efficacy 2.32558139534884 2.38095238095238 2.38095238095238
#> 5 Withdrew Consent 1.16279069767442 1.19047619047619 1.19047619047619
#> 6 Participants Ongoing 72.09302 85.71429 76.19048
#> Total var_label
#> 1 22.04724 Trial Disposition
#> 2 15.748031496063 Trial Disposition
#> 3 2.75590551181102 Trial Disposition
#> 4 2.36220472440945 Trial Disposition
#> 5 1.18110236220472 Trial Disposition
#> 6 77.95276 Trial Disposition
#>
#> [[2]]
#> name Placebo Low Dose High Dose
#> 1 Completed 67.44186 29.7619 32.14286
#> 2 Discontinued 32.55814 70.2381 67.85714
#> 3 Adverse Event 9.30232558139535 52.3809523809524 47.6190476190476
#> 4 Death 2.32558139534884 1.19047619047619 0
#> 5 I/E Not Met 1.16279069767442 0 2.38095238095238
#> 6 Lack of Efficacy 3.48837209302326 0 1.19047619047619
#> 7 Lost to Follow-up 1.16279069767442 1.19047619047619 0
#> 8 Physician Decision 1.16279069767442 0 2.38095238095238
#> 9 Protocol Violation 1.16279069767442 1.19047619047619 1.19047619047619
#> 10 Sponsor Decision 2.32558139534884 2.38095238095238 3.57142857142857
#> 11 Withdrew Consent 10.4651162790698 11.9047619047619 9.52380952380952
#> Total var_label
#> 1 43.30709 Participant Study Medication Disposition
#> 2 56.69291 Participant Study Medication Disposition
#> 3 36.2204724409449 Participant Study Medication Disposition
#> 4 1.18110236220472 Participant Study Medication Disposition
#> 5 1.18110236220472 Participant Study Medication Disposition
#> 6 1.5748031496063 Participant Study Medication Disposition
#> 7 0.78740157480315 Participant Study Medication Disposition
#> 8 1.18110236220472 Participant Study Medication Disposition
#> 9 1.18110236220472 Participant Study Medication Disposition
#> 10 2.75590551181102 Participant Study Medication Disposition
#> 11 10.6299212598425 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 Discontinued 24 (27.9) 12 (14.3) 20 (23.8) 56 (22.0)
#> 3 Adverse Event 18 (20.9) 9 (10.7) 13 (15.5) 40 (15.7)
#> 4 I/E Not Met 3 (3.5) 0 (0.0) 4 (4.8) 7 (2.8)
#> 5 Lack of Efficacy 2 (2.3) 2 (2.4) 2 (2.4) 6 (2.4)
#> 6 Withdrew Consent 1 (1.2) 1 (1.2) 1 (1.2) 3 (1.2)
#> 7 Participants Ongoing 62 (72.1) 72 (85.7) 64 (76.2) 198 (78.0)
#> 8 Completed 58 (67.4) 25 (29.8) 27 (32.1) 110 (43.3)
#> 9 Discontinued 28 (32.6) 59 (70.2) 57 (67.9) 144 (56.7)
#> 10 Adverse Event 8 (9.3) 44 (52.4) 40 (47.6) 92 (36.2)
#> 11 Death 2 (2.3) 1 (1.2) 0 (0.0) 3 (1.2)
#> 12 I/E Not Met 1 (1.2) 0 (0.0) 2 (2.4) 3 (1.2)
#> 13 Lack of Efficacy 3 (3.5) 0 (0.0) 1 (1.2) 4 (1.6)
#> 14 Lost to Follow-up 1 (1.2) 1 (1.2) 0 (0.0) 2 (0.8)
#> 15 Physician Decision 1 (1.2) 0 (0.0) 2 (2.4) 3 (1.2)
#> 16 Protocol Violation 1 (1.2) 1 (1.2) 1 (1.2) 3 (1.2)
#> 17 Sponsor Decision 2 (2.3) 2 (2.4) 3 (3.6) 7 (2.8)
#> 18 Withdrew Consent 9 (10.5) 10 (11.9) 8 (9.5) 27 (10.6)
#> var_label
#> 1 -----
#> 2 Trial Disposition
#> 3 Trial Disposition
#> 4 Trial Disposition
#> 5 Trial Disposition
#> 6 Trial Disposition
#> 7 Trial Disposition
#> 8 Participant Study Medication 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 Disposition
#> 16 Participant Study Medication Disposition
#> 17 Participant Study Medication Disposition
#> 18 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/RtmpIQOeKx/file1949135d845d.Rdata
#> The output is saved in/home/runner/work/metalite.sl/metalite.sl/vignettes/outtable/disposition.rtf