random.cdisc.data Package

Description

Package to create random SDTM and ADAM datasets.

Author(s)

Maintainer: Joe Zhu joe.zhu@roche.com

Authors:

• Pawel Rucki pawel.rucki@roche.com

• Nick Paszty npaszty@gene.com

• Jana Stoilova jana.stoilova@roche.com

• Davide Garolini davide.garolini@roche.com

• Emily de la Rua emily.de_la_rua@contractors.roche.com

• Christopher DiPietrantonio

• Adrian Waddell adrian.waddell@gene.com

Other contributors:

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/random.cdisc.data/

• https://github.com/insightsengineering/random.cdisc.data/

• Report bugs at https://github.com/insightsengineering/random.cdisc.data/issues

Apply Metadata

Description

Apply label and variable ordering attributes to domains.

Usage

apply_metadata(
  df,
  filename,
  add_adsl = TRUE,
  adsl_filename = "metadata/ADSL.yml"
)

Arguments

Value

Data frame with metadata applied.

Examples

seed <- 1
adsl <- radsl(seed = seed)
adsub <- radsub(adsl, seed = seed)
yaml_path <- file.path(path.package("random.cdisc.data"), "inst", "metadata")
adsl <- apply_metadata(adsl, file.path(yaml_path, "ADSL.yml"), FALSE)
adsub <- apply_metadata(
  adsub, file.path(yaml_path, "ADSUB.yml"), TRUE,
  file.path(yaml_path, "ADSL.yml")
)

Standard Arguments

Description

The documentation to this function lists all the arguments in random.cdisc.data that are used repeatedly in dataset creation.

Arguments

Cached ADAB

Description

Cached ADAB data generated with seed = 1

Usage

data(cadab)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 6916 rows and 21 columns.

Cached ADAE

Description

Cached ADAE data generated with seed = 1

Usage

data(cadae)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1934 rows and 92 columns.

Cached ADAETTE

Description

Cached ADAETTE data generated with seed = 1

Usage

data(cadaette)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3600 rows and 66 columns.

Cached ADCM

Description

Cached ADCM data generated with seed = 1

Usage

data(cadcm)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3685 rows and 83 columns.

Cached ADDV

Description

Cached ADDV data generated with seed = 1

Usage

data(caddv)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 119 rows and 66 columns.

Cached ADEG

Description

Cached ADEG data generated with seed = 1

Usage

data(cadeg)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 13600 rows and 88 columns.

Cached ADEX

Description

Cached ADEX data generated with seed = 1

Usage

data(cadex)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 6400 rows and 79 columns.

Cached ADHY

Description

Cached ADHY data generated with seed = 1

Usage

data(cadhy)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 20000 rows and 71 columns.

Cached ADLB

Description

Cached ADLB data generated with seed = 1

Usage

data(cadlb)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 8400 rows and 102 columns.

Cached ADMH

Description

Cached ADMH data generated with seed = 1

Usage

data(cadmh)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1934 rows and 67 columns.

Cached ADPC

Description

Cached ADPC data generated with seed = 1

Usage

data(cadpc)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 6640 rows and 72 columns.

Cached ADPP

Description

Cached ADPP data generated with seed = 1

Usage

data(cadpp)

Format

An object of class data.frame with 26268 rows and 68 columns.

Cached ADQLQC

Description

Cached ADQLQC data generated with seed = 1

Usage

data(cadqlqc)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 116803 rows and 50 columns.

Cached ADQS

Description

Cached ADQS data generated with seed = 1

Usage

data(cadqs)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 14000 rows and 73 columns.

Cached ADRS

Description

Cached ADRS data generated with seed = 1

Usage

data(cadrs)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3200 rows and 65 columns.

Cached ADSL

Description

Cached ADSL data generated with seed = 1

Usage

data(cadsl)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 400 rows and 55 columns.

Cached ADSUB

Description

Cached ADSUB data generated with seed = 1

Usage

data(cadsub)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2000 rows and 65 columns.

Cached ADTR

Description

Cached ADTR data generated with seed = 1

Usage

data(cadtr)

Format

An object of class data.frame with 2800 rows and 76 columns.

Cached ADTTE

Description

Cached ADTTE data generated with seed = 1

Usage

data(cadtte)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2000 rows and 67 columns.

Cached ADVS

Description

Cached ADVS data generated with seed = 1

Usage

data(cadvs)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 16800 rows and 87 columns.

Helper Functions for Constructing ADQLQC

Description

Internal functions used by radqlqc.

Usage

get_qs_data(
  adsl,
  visit_format = "CYCLE",
  n_assessments = 5L,
  n_days = 1L,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(QSORRES = c(1234, 0.2), QSSTRESC = c(1234, 0.2))
)

get_random_dates_between(from, to, visit_id)

prep_adqlqc(df)

calc_scales(adqlqc1)

derv_chgcat1(dataset)

comp_derv(dataset, percent, number)

Arguments

Value

a dataframe with SDTM questionnaire data

Data frame with new randomly generated dates variable.

data.frame

data.frame

data.frame

data.frame

Functions

• get_qs_data(): Questionnaires EORTC QLQ-C30 V3.0 SDTM (QS)

  Function for generating random Questionnaires SDTM domain

• get_random_dates_between(): Function for generating random dates between 2 dates

• prep_adqlqc(): Prepare ADaM ADQLQC data, adding PARAMCD to SDTM QS data

• calc_scales(): Scale calculation for ADQLQC data

• derv_chgcat1(): Calculate Change from Baseline Category 1

• comp_derv(): Completion/Compliance Data Calculation

Examples

adsl <- radsl(N = 10, study_duration = 2, seed = 1)
adqlqc <- radqlqc(adsl, seed = 1, percent = 80, number = 2)

Generate Anthropometric Measurements for Males and Females.

Description

Anthropometric measurements are randomly generated using normal approximation. The default mean and standard deviation values used are based on US National Health Statistics for adults aged 20 years or over. The measurements are generated in same units as provided to the function.

Usage

h_anthropometrics_by_sex(
  df,
  seed = 1,
  id_var = "USUBJID",
  sex_var = "SEX",
  sex_var_level_male = "M",
  male_weight_in_kg = list(mean = 90.6, sd = 44.9),
  female_weight_in_kg = list(mean = 77.5, sd = 46.2),
  male_height_in_m = list(mean = 1.75, sd = 0.14),
  female_height_in_m = list(mean = 1.61, sd = 0.24)
)

Arguments

Details

One record per subject.

Value

a dataframe with anthropometric measurements for each subject in analysis dataset.

Replace Values with NA

Description

Replace column values with NAs.

Usage

mutate_na(ds, na_vars = NULL, na_percentage = 0.05)

Arguments

Value

dataframe without NA values.

Anti-Drug Antibody Analysis Dataset (ADAB)

Description

Function for generating a random Anti-Drug Antibody Analysis Dataset for a given Subject-Level Analysis Dataset and Pharmacokinetics Analysis Dataset.

Usage

radab(
  adsl,
  adpc,
  constants = c(D = 100, ka = 0.8, ke = 1),
  paramcd = c("R1800000", "RESULT1", "R1800001", "RESULT2", "ADASTAT1", "INDUCD1",
    "ENHANC1", "TRUNAFF1", "EMERNEG1", "EMERPOS1", "PERSADA1", "TRANADA1", "BFLAG1",
    "TIMADA1", "ADADUR1", "ADASTAT2", "INDUCD2", "ENHANC2", "EMERNEG2", "EMERPOS2",
    "BFLAG2", "TRUNAFF2"),
  param = c("Antibody titer units", "ADA interpreted per sample result",
    "Neutralizing Antibody titer units", "NAB interpreted per sample result",
    "ADA Status of a patient", "Treatment induced ADA", "Treatment enhanced ADA",
    "Treatment unaffected", "Treatment Emergent - Negative",
    "Treatment Emergent - Positive", "Persistent ADA", "Transient ADA", "Baseline",
    "Time to onset of ADA", "ADA Duration", "NAB Status of a patient",
    "Treatment induced ADA, Neutralizing Antibody",
    "Treatment enhanced ADA, Neutralizing Antibody", 
    
    "Treatment Emergent - Negative, Neutralizing Antibody",
    "Treatment Emergent - Positive, Neutralizing Antibody",
    "Baseline, Neutralizing Antibody", "Treatment unaffected, Neutralizing Antibody"),
  avalu = c("titer", "", "titer", "", "", "", "", "", "", "", "", "", "", "weeks",
    "weeks", "", "", "", "", "", "", ""),
  seed = NULL,
  na_percentage = 0,
  na_vars = list(AVAL = c(NA, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per study per subject per parameter per time point: "R1800000", "RESULT1", "R1800001", "RESULT2".

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)
adpc <- radpc(adsl, seed = 2, duration = 9 * 7)

adab <- radab(adsl, adpc, seed = 2)
adab

Adverse Event Analysis Dataset (ADAE)

Description

Function for generating random Adverse Event Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radae(
  adsl,
  max_n_aes = 10L,
  lookup = NULL,
  lookup_aag = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(AEBODSYS = c(NA, 0.1), AEDECOD = c(1234, 0.1), AETOXGR = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per each record in the corresponding SDTM domain.

Keys: STUDYID, USUBJID, ASTDTM, AETERM, AESEQ

Value

data.frame

Examples

adsl <- radsl(N = 10, study_duration = 2, seed = 1)

adae <- radae(adsl, seed = 2)
adae

# Add metadata.
aag <- utils::read.table(
  sep = ",", header = TRUE,
  text = paste(
    "NAMVAR,SRCVAR,GRPTYPE,REFNAME,REFTERM,SCOPE",
    "CQ01NAM,AEDECOD,CUSTOM,D.2.1.5.3/A.1.1.1.1 AESI,dcd D.2.1.5.3,",
    "CQ01NAM,AEDECOD,CUSTOM,D.2.1.5.3/A.1.1.1.1 AESI,dcd A.1.1.1.1,",
    "SMQ01NAM,AEDECOD,SMQ,C.1.1.1.3/B.2.2.3.1 AESI,dcd C.1.1.1.3,BROAD",
    "SMQ01NAM,AEDECOD,SMQ,C.1.1.1.3/B.2.2.3.1 AESI,dcd B.2.2.3.1,BROAD",
    "SMQ02NAM,AEDECOD,SMQ,Y.9.9.9.9/Z.9.9.9.9 AESI,dcd Y.9.9.9.9,NARROW",
    "SMQ02NAM,AEDECOD,SMQ,Y.9.9.9.9/Z.9.9.9.9 AESI,dcd Z.9.9.9.9,NARROW",
    sep = "\n"
  ), stringsAsFactors = FALSE
)

adae <- radae(adsl, lookup_aag = aag)

with(
  adae,
  cbind(
    table(AEDECOD, SMQ01NAM),
    table(AEDECOD, CQ01NAM)
  )
)

Time to Adverse Event Analysis Dataset (ADAETTE)

Description

Function to generate random Time-to-AE Dataset for a given Subject-Level Analysis Dataset.

Usage

radaette(
  adsl,
  event_descr = NULL,
  censor_descr = NULL,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(CNSR = c(NA, 0.1), AVAL = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

Keys: STUDYID, USUBJID, PARAMCD

Value

data.frame

Author(s)

Xiuting Mi

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adaette <- radaette(adsl, seed = 2)
adaette

Previous and Concomitant Medications Analysis Dataset (ADCM)

Description

Function for generating random Concomitant Medication Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radcm(
  adsl,
  max_n_cms = 10L,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(CMCLAS = c(NA, 0.1), CMDECOD = c(1234, 0.1), ATIREL = c(1234, 0.1)),
  who_coding = FALSE,
  cached = FALSE
)

Arguments

Details

One record per each record in the corresponding SDTM domain.

Keys: STUDYID, USUBJID, ASTDTM, CMSEQ

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adcm <- radcm(adsl, seed = 2)
adcm

adcm_who <- radcm(adsl, seed = 2, who_coding = TRUE)
adcm_who

Protocol Deviations Analysis Dataset (ADDV)

Description

Function for generating random Protocol Deviations Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

raddv(
  adsl,
  max_n_dv = 3L,
  p_dv = 0.15,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(ASTDT = c(seed = 1234, percentage = 0.1), DVCAT = c(seed = 1234,
    percentage = 0.1)),
  cached = FALSE
)

Arguments

Details

One record per each record in the corresponding SDTM domain.

Keys: STUDYID, USUBJID, ASTDT, DVTERM, DVSEQ

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

addv <- raddv(adsl, seed = 2)
addv

ECG Analysis Dataset (ADEG)

Description

Function for generating random dataset from ECG Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radeg(
  adsl,
  egcat = c("INTERVAL", "INTERVAL", "MEASUREMENT", "FINDING"),
  param = c("QT Duration", "RR Duration", "Heart Rate", "ECG Interpretation"),
  paramcd = c("QT", "RR", "HR", "ECGINTP"),
  paramu = c("msec", "msec", "beats/min", ""),
  visit_format = "WEEK",
  n_assessments = 5L,
  n_days = 5L,
  max_n_eg = 10L,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(ABLFL = c(1235, 0.1), BASE = c(NA, 0.1), BASEC = c(NA, 0.1), CHG =
    c(1234, 0.1), PCHG = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date.

Keys: STUDYID, USUBJID, PARAMCD, BASETYPE, AVISITN, ATPTN, DTYPE, ADTM, EGSEQ, ASPID

Value

data.frame

Author(s)

tomlinsj, npaszty, Xuefeng Hou, dipietrc

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adeg <- radeg(adsl, visit_format = "WEEK", n_assessments = 7L, seed = 2)
adeg

adeg <- radeg(adsl, visit_format = "CYCLE", n_assessments = 2L, seed = 2)
adeg

Exposure Analysis Dataset (ADEX)

Description

Function for generating random Exposure Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radex(
  adsl,
  param = c("Dose administered during constant dosing interval",
    "Number of doses administered during constant dosing interval",
    "Total dose administered", "Total number of doses administered"),
  paramcd = c("DOSE", "NDOSE", "TDOSE", "TNDOSE"),
  paramu = c("mg", " ", "mg", " "),
  parcat1 = c("INDIVIDUAL", "OVERALL"),
  parcat2 = c("Drug A", "Drug B"),
  visit_format = "WEEK",
  n_assessments = 5L,
  n_days = 5L,
  max_n_exs = 6L,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(AVAL = c(NA, 0.1), AVALU = c(NA), 0.1),
  cached = FALSE
)

Arguments

Details

One record per each record in the corresponding SDTM domain.

Keys: STUDYID, USUBJID, EXSEQ, PARAMCD, PARCAT1, ASTDTM, AENDTM, ASTDY, AENDY, AVISITN, EXDOSFRQ, EXROUTE, VISIT, VISITDY, EXSTDTC, EXENDTC, EXSTDY, EXENDY

Value

data.frame

Examples

adsl <- radsl(N = 10, study_duration = 2, seed = 1)

adex <- radex(adsl, seed = 2)
adex

Hy's Law Analysis Dataset (ADHY)

Description

Function for generating a random Hy's Law Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radhy(
  adsl,
  param = c("TBILI <= 2 times ULN and ALT value category",
    "TBILI > 2 times ULN and AST value category",
    "TBILI > 2 times ULN and ALT value category",
    "TBILI <= 2 times ULN and AST value category",
    "TBILI > 2 times ULN and ALKPH <= 2 times ULN and ALT value category",
    "TBILI > 2 times ULN and ALKPH <= 2 times ULN and AST value category",
    "TBILI > 2 times ULN and ALKPH <= 5 times ULN and ALT value category",
    "TBILI > 2 times ULN and ALKPH <= 5 times ULN and AST value category",
    "TBILI <= 2 times ULN and two consecutive elevations of ALT in relation to ULN", 
   
     "TBILI > 2 times ULN and two consecutive elevations of AST in relation to ULN",
    "TBILI <= 2 times ULN and two consecutive elevations of AST in relation to ULN",
    "TBILI > 2 times ULN and two consecutive elevations of ALT in relation to ULN",
    "TBILI > 2 times ULN and two consecutive elevations of ALT in relation to Baseline",
    "TBILI <= 2 times ULN and two consecutive elevations of ALT in relation to Baseline",
    "TBILI > 2 times ULN and two consecutive elevations of AST in relation to Baseline",
    
    
    "TBILI <= 2 times ULN and two consecutive elevations of AST in relation to Baseline",
    "ALT > 3 times ULN by Period", "AST > 3 times ULN by Period",
    "ALT or AST > 3 times ULN by Period", "ALT > 3 times Baseline by Period",
    "AST > 3 times Baseline by Period", "ALT or AST > 3 times Baseline by Period"),
  paramcd = c("BLAL", "BGAS", "BGAL", "BLAS", "BA2AL", "BA2AS", "BA5AL", "BA5AS",
    "BL2AL2CU", "BG2AS2CU", "BL2AS2CU", "BG2AL2CU", "BG2AL2CB", "BL2AL2CB", "BG2AS2CB",
    "BL2AS2CB", "ALTPULN", "ASTPULN", "ALTASTPU", "ALTPBASE", "ASTPBASE", "ALTASTPB"),
  seed = NULL,
  cached = FALSE
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date.

Keys: STUDYID, USUBJID, PARAMCD, AVISITN, ADTM, SRCSEQ

Value

data.frame

Author(s)

wojciakw

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adhy <- radhy(adsl, seed = 2)
adhy

Laboratory Data Analysis Dataset (ADLB)

Description

Function for generating a random Laboratory Data Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radlb(
  adsl,
  lbcat = c("CHEMISTRY", "CHEMISTRY", "IMMUNOLOGY"),
  param = c("Alanine Aminotransferase Measurement", "C-Reactive Protein Measurement",
    "Immunoglobulin A Measurement"),
  paramcd = c("ALT", "CRP", "IGA"),
  paramu = c("U/L", "mg/L", "g/L"),
  aval_mean = c(18, 9, 2.9),
  visit_format = "WEEK",
  n_assessments = 5L,
  n_days = 5L,
  max_n_lbs = 10L,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(LOQFL = c(NA, 0.1), ABLFL2 = c(1234, 0.1), ABLFL = c(1235, 0.1), BASE2 =
    c(NA, 0.1), BASE = c(NA, 0.1), CHG2 = c(1235, 0.1), PCHG2 = c(1235, 0.1), CHG =
    c(1234, 0.1), PCHG = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date.

Keys: STUDYID, USUBJID, PARAMCD, BASETYPE, AVISITN, ATPTN, DTYPE, ADTM, LBSEQ, ASPID

Value

data.frame

Author(s)

tomlinsj, npaszty, Xuefeng Hou

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adlb <- radlb(adsl, visit_format = "WEEK", n_assessments = 7L, seed = 2)
adlb

adlb <- radlb(adsl, visit_format = "CYCLE", n_assessments = 2L, seed = 2)
adlb

Medical History Analysis Dataset (ADMH)

Description

Function for generating a random Medical History Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radmh(
  adsl,
  max_n_mhs = 10L,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(MHBODSYS = c(NA, 0.1), MHDECOD = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per each record in the corresponding SDTM domain.

Keys: STUDYID, USUBJID, ASTDTM, MHSEQ

Value

data.frame

Examples

adsl <- radsl(N = 10, study_duration = 2, seed = 1)

admh <- radmh(adsl, seed = 2)
admh

Pharmacokinetics Analysis Dataset (ADPC)

Description

Function for generating a random Pharmacokinetics Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radpc(
  adsl,
  avalu = "ug/mL",
  constants = c(D = 100, ka = 0.8, ke = 1),
  duration = 2,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(AVAL = c(NA, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per study, subject, parameter, and time point.

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adpc <- radpc(adsl, seed = 2)
adpc

adpc <- radpc(adsl, seed = 2, duration = 3)
adpc

Pharmacokinetics Parameters Dataset (ADPP)

Description

Function for generating a random Pharmacokinetics Parameters Dataset for a given Subject-Level Analysis Dataset.

Usage

radpp(
  adsl,
  ppcat = c("Plasma Drug X", "Plasma Drug Y", "Metabolite Drug X", "Metabolite Drug Y"),
  ppspec = c("Plasma", "Plasma", "Plasma", "Matrix of PD", "Matrix of PD", "Urine",
    "Urine", "Urine", "Urine"),
  paramcd = c("AUCIFO", "CMAX", "CLO", "RMAX", "TON", "RENALCL", "RENALCLD", "RCAMINT",
    "RCPCINT"),
  param = c("AUC Infinity Obs", "Max Conc", "Total CL Obs", "Time of Maximum Response",
    "Time to Onset", "Renal CL", "Renal CL Norm by Dose", "Amt Rec from T1 to T2",
    "Pct Rec from T1 to T2"),
  paramu = c("day*ug/mL", "ug/mL", "ml/day/kg", "hr", "hr", "L/hr", "L/hr/mg", "mg",
    "%"),
  aval_mean = c(200, 30, 5, 10, 3, 0.05, 0.005, 1.5613, 15.65),
  visit_format = "CYCLE",
  n_days = 2L,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(AVAL = c(NA, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per study, subject, parameter category, parameter and visit.

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adpp <- radpp(adsl, seed = 2)
adpp

EORTC QLQ-C30 V3 Analysis Dataset (ADQLQC)

Description

Function for generating a random EORTC QLQ-C30 V3 Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radqlqc(adsl, percent, number, seed = NULL, cached = FALSE)

Arguments

Details

Keys: STUDYID, USUBJID, PARCAT1N, PARAMCD, BASETYPE, AVISITN, ATPTN, ADTM, QSSEQ

Value

data.frame

Examples

adsl <- radsl(N = 10, study_duration = 2, seed = 1)

adqlqc <- radqlqc(adsl, seed = 1, percent = 80, number = 2)
adqlqc

Questionnaires Analysis Dataset (ADQS)

Description

Function for generating a random Questionnaires Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radqs(
  adsl,
  param = c("BFI All Questions", "Fatigue Interference",
    "Function/Well-Being (GF1,GF3,GF7)", "Treatment Side Effects (GP2,C5,GP5)",
    "FKSI-19 All Questions"),
  paramcd = c("BFIALL", "FATIGI", "FKSI-FWB", "FKSI-TSE", "FKSIALL"),
  visit_format = "WEEK",
  n_assessments = 5L,
  n_days = 5L,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(LOQFL = c(NA, 0.1), ABLFL2 = c(1234, 0.1), ABLFL = c(1235, 0.1), CHG2 =
    c(1235, 0.1), PCHG2 = c(1235, 0.1), CHG = c(1234, 0.1), PCHG = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date.

Keys: STUDYID, USUBJID, PARAMCD, AVISITN

Value

data.frame

Author(s)

npaszty

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adqs <- radqs(adsl, visit_format = "WEEK", n_assessments = 7L, seed = 2)
adqs

adqs <- radqs(adsl, visit_format = "CYCLE", n_assessments = 3L, seed = 2)
adqs

Tumor Response Analysis Dataset (ADRS)

Description

Function for generating a random Tumor Response Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radrs(
  adsl,
  avalc = NULL,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(AVISIT = c(NA, 0.1), AVAL = c(1234, 0.1), AVALC = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date. SDTM variables are populated on new records coming from other single records. Otherwise, SDTM variables are left blank.

Keys: STUDYID, USUBJID, PARAMCD, AVISITN, ADT, RSSEQ

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adrs <- radrs(adsl, seed = 2)
adrs

Time to Safety Event Analysis Dataset (ADSAFTTE)

Description

Function to generate random Time-to-Safety Event Dataset for a given Subject-Level Analysis Dataset.

Usage

radsaftte(adsl, ...)

Arguments

Details

Keys: STUDYID, USUBJID, PARAMCD

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adsaftte <- radsaftte(adsl, seed = 2)
adsaftte

Subject-Level Analysis Dataset (ADSL)

Description

The Subject-Level Analysis Dataset (ADSL) is used to provide the variables that describe attributes of a subject. ADSL is a source for subject-level variables used in other analysis data sets, such as population flags and treatment variables. There is only one ADSL per study. ADSL and its related metadata are required in a CDISC-based submission of data from a clinical trial even if no other analysis data sets are submitted.

Usage

radsl(
  N = 400,
  study_duration = 2,
  seed = NULL,
  with_trt02 = TRUE,
  na_percentage = 0,
  na_vars = list(AGE = NA, SEX = NA, RACE = NA, STRATA1 = NA, STRATA2 = NA, BMRKR1 =
    c(seed = 1234, percentage = 0.1), BMRKR2 = c(1234, 0.1), BEP01FL = NA),
  ae_withdrawal_prob = 0.05,
  cached = FALSE
)

Arguments

Details

One record per subject.

Keys: STUDYID, USUBJID

Value

data.frame

Examples

adsl <- radsl(N = 10, study_duration = 2, seed = 1)
adsl

adsl <- radsl(
  N = 10, seed = 1,
  na_percentage = 0.1,
  na_vars = list(
    DTHDT = c(seed = 1234, percentage = 0.1),
    LSTALVDT = c(seed = 1234, percentage = 0.1)
  )
)
adsl

adsl <- radsl(N = 10, seed = 1, na_percentage = .1)
adsl

Subcategory Analysis Dataset (ADSUB)

Description

Function for generating a random Subcategory Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radsub(
  adsl,
  param = c("Baseline Weight", "Baseline Height", "Baseline BMI", "Baseline ECOG",
    "Baseline Biomarker Mutation"),
  paramcd = c("BWGHTSI", "BHGHTSI", "BBMISI", "BECOG", "BBMRKR1"),
  seed = NULL,
  na_percentage = 0,
  na_vars = list(),
  cached = FALSE
)

Arguments

Details

One record per subject.

Keys: STUDYID, USUBJID, PARAMCD, AVISITN, ADTM, SRCSEQ

Value

data.frame

Author(s)

tomlinsj, npaszty, Xuefeng Hou, dipietrc

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adsub <- radsub(adsl, seed = 2)
adsub

Tumor Response Analysis Dataset (ADTR)

Description

Function for generating a random Tumor Response Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radtr(
  adsl,
  param = c("Sum of Longest Diameter by Investigator"),
  paramcd = c("SLDINV"),
  seed = NULL,
  cached = FALSE,
  ...
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date.

Keys: STUDYID, USUBJID, PARAMCD, BASETYPE, AVISITN, DTYPE

Value

data.frame

Author(s)

tomlinsj, npaszty, Xuefeng Hou, dipietrc

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adtr <- radtr(adsl, seed = 2)
adtr

Time-to-Event Analysis Dataset (ADTTE)

Description

Function for generating a random Time-to-Event Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radtte(
  adsl,
  event_descr = NULL,
  censor_descr = NULL,
  lookup = NULL,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(CNSR = c(NA, 0.1), AVAL = c(1234, 0.1), AVALU = c(1234, 0.1)),
  cached = FALSE
)

Arguments

Details

Keys: STUDYID, USUBJID, PARAMCD

Value

data.frame

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

adtte <- radtte(adsl, seed = 2)
adtte

Vital Signs Analysis Dataset (ADVS)

Description

Function for generating a random Vital Signs Analysis Dataset for a given Subject-Level Analysis Dataset.

Usage

radvs(
  adsl,
  param = c("Diastolic Blood Pressure", "Pulse Rate", "Respiratory Rate",
    "Systolic Blood Pressure", "Temperature", "Weight"),
  paramcd = c("DIABP", "PULSE", "RESP", "SYSBP", "TEMP", "WEIGHT"),
  paramu = c("Pa", "beats/min", "breaths/min", "Pa", "C", "Kg"),
  visit_format = "WEEK",
  n_assessments = 5L,
  n_days = 5L,
  seed = NULL,
  na_percentage = 0,
  na_vars = list(CHG2 = c(1235, 0.1), PCHG2 = c(1235, 0.1), CHG = c(1234, 0.1), PCHG =
    c(1234, 0.1), AVAL = c(123, 0.1), AVALU = c(123, 0.1)),
  cached = FALSE
)

Arguments

Details

One record per subject per parameter per analysis visit per analysis date.

Keys: STUDYID, USUBJID, PARAMCD, BASETYPE, AVISITN, ATPTN, DTYPE, ADTM, VSSEQ, ASPID

Value

data.frame

Author(s)

npaszty

Examples

adsl <- radsl(N = 10, seed = 1, study_duration = 2)

advs <- radvs(adsl, visit_format = "WEEK", n_assessments = 7L, seed = 2)
advs

advs <- radvs(adsl, visit_format = "CYCLE", n_assessments = 3L, seed = 2)
advs

Primary Keys: Labels

Description

Shallow copy of formatters::var_relabel(). Used mainly internally to relabel a subset of variables in a data set.

Usage

rcd_var_relabel(x, ...)

Arguments

Value

x (data.frame)
Data frame with labels applied.

Related Variables: Assign

Description

Assign values to a related variable within a domain.

Usage

rel_var(df, var_name, related_var, var_values = NULL)

Arguments

Value

df with added factor variable var_name containing var_values corresponding to related_var.

Examples

# Example with data.frame.
params <- c("Level A", "Level B", "Level C")
adlb_df <- data.frame(
  ID = 1:9,
  PARAM = factor(
    rep(c("Level A", "Level B", "Level C"), 3),
    levels = params
  )
)
rel_var(
  df = adlb_df,
  var_name = "PARAMCD",
  var_values = c("A", "B", "C"),
  related_var = "PARAM"
)

# Example with tibble.
adlb_tbl <- tibble::tibble(
  ID = 1:9,
  PARAM = factor(
    rep(c("Level A", "Level B", "Level C"), 3),
    levels = params
  )
)
rel_var(
  df = adlb_tbl,
  var_name = "PARAMCD",
  var_values = c("A", "B", "C"),
  related_var = "PARAM"
)

Related Variables: Initialize

Description

Verify and initialize related variable values. For example, relvar_init("Alanine Aminotransferase Measurement", "ALT").

Usage

relvar_init(relvar1, relvar2)

Arguments

Value

A vector of n elements.

Replace Values in a Vector by NA

Description

Randomized replacement of values by NA.

Usage

replace_na(v, percentage = 0.05, seed = NULL)

Arguments

Value

The input vector v where a certain number of values are replaced by NA.

Primary Keys: Retain Values

Description

Retain values within primary keys.

Usage

retain(df, value_var, event, outside = NA)

Arguments

Value

A vector of values where expression is true.

Truncated Exponential Distribution

Description

This generates random numbers from a truncated Exponential distribution, i.e. from X | X > l or X | X < r when X ~ Exp(rate). The advantage here is that we guarantee to return exactly n numbers and without using a loop internally. This can be derived from the quantile functions of the left- and right-truncated Exponential distributions.

Usage

rtexp(n, rate, l = NULL, r = NULL)

Arguments

Value

The random numbers. If neither l nor r are provided then the usual Exponential distribution is used.

Examples

x <- stats::rexp(1e6, rate = 5)
x <- x[x > 0.5]
hist(x)

y <- rtexp(1e6, rate = 5, l = 0.5)
hist(y)

z <- rtexp(1e6, rate = 5, r = 0.5)
hist(z)

Zero-Truncated Poisson Distribution

Description

This generates random numbers from a zero-truncated Poisson distribution, i.e. from X | X > 0 when X ~ Poisson(lambda). The advantage here is that we guarantee to return exactly n numbers and without using a loop internally. This solution was provided in a post by Peter Dalgaard.

Usage

rtpois(n, lambda)

Arguments

Value

The random numbers.

Examples

x <- rpois(1e6, lambda = 5)
x <- x[x > 0]
hist(x)

y <- rtpois(1e6, lambda = 5)
hist(y)

Create a Factor with Random Elements of x

Description

Sample elements from x with replacement to build a factor.

Usage

sample_fct(x, N, ...)

Arguments

Value

A factor of length N.

Examples

sample_fct(letters[1:3], 10)
sample_fct(iris$Species, 10)

Create Visit Schedule

Description

Create a visit schedule as a factor.

Usage

visit_schedule(visit_format = "WEEK", n_assessments = 10L, n_days = 5L)

Arguments

Details

X number of visits, or X number of cycles and Y number of days.

Value

A factor of length n_assessments.

Examples

visit_schedule(visit_format = "WEeK", n_assessments = 10L)
visit_schedule(visit_format = "CyCLE", n_assessments = 5L, n_days = 2L)