ADPC

The Non-compartmental analysis (NCA) ADaM uses the CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0). This example presented uses underlying EX and PC domains where the EX and PC domains represent data as collected and the ADPC ADaM is output. For more details see the admiral vignette.

First Load Packages

First we will load the packages required for our project. We will use admiral for the creation of analysis data. admiral requires dplyr, lubridate and stringr. Find other admiral functions and related variables by searching admiraldiscovery. We will use metacore and metatools to store and manipulate metadata from our specifications. We will use xportr to perform checks on the final data and export to a transport file.

The source SDTM data will come from the CDISC pilot study data stored in pharmaversesdtm.

# Load Packages
library(admiral)
library(dplyr)
library(lubridate)
library(stringr)
library(metacore)
library(metatools)
library(xportr)
library(pharmaversesdtm)
library(pharmaverseadam)
library(reactable)

Next Load Specifications for Metacore

We have saved our specifications in an Excel file and will load them into metacore with the metacore::spec_to_metacore() function.

# ---- Load Specs for Metacore ----

metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
  select_dataset("ADPC")

Load Source Datasets

We will load are SDTM data from pharmaversesdtm. The main components of this will be exposure data from EX and pharmacokinetic concentration data from PC. We will use ADSL for baseline characteristics and we will derive additional baselines from vital signs VS.

# ---- Load source datasets ----
# Load PC, EX, VS, LB and ADSL
ex <- pharmaversesdtm::ex
pc <- pharmaversesdtm::pc
vs <- pharmaversesdtm::vs

adsl <- pharmaverseadam::adsl

ex <- convert_blanks_to_na(ex)
pc <- convert_blanks_to_na(pc)
vs <- convert_blanks_to_na(vs)

Derivations

Derive PC Dates

Here we use admiral functions for working with dates and we will also create a nominal time from first dose NFRLT for PC data based on PCTPTNUM.

# Get list of ADSL vars required for derivations
adsl_vars <- exprs(TRTSDT, TRTSDTM, TRT01P, TRT01A)

pc_dates <- pc %>%
  # Join ADSL with PC (need TRTSDT for ADY derivation)
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = adsl_vars,
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  # Derive analysis date/time
  # Impute missing time to 00:00:00
  derive_vars_dtm(
    new_vars_prefix = "A",
    dtc = PCDTC,
    time_imputation = "00:00:00"
  ) %>%
  # Derive dates and times from date/times
  derive_vars_dtm_to_dt(exprs(ADTM)) %>%
  derive_vars_dtm_to_tm(exprs(ADTM)) %>%
  derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%
  # Derive event ID and nominal relative time from first dose (NFRLT)
  mutate(
    EVID = 0,
    DRUG = PCTEST,
    NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID
  )

The default value of `ignore_seconds_flag` will change to "TRUE" in admiral
1.4.0.

Sample of Data

Get Dosing Information

Here we also create nominal time from first dose NFRLT for EX data based on VISITDY.

ex_dates <- ex %>%
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = adsl_vars,
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  # Keep records with nonzero dose
  filter(EXDOSE > 0) %>%
  # Add time and set missing end date to start date
  # Impute missing time to 00:00:00
  # Note all times are missing for dosing records in this example data
  # Derive Analysis Start and End Dates
  derive_vars_dtm(
    new_vars_prefix = "AST",
    dtc = EXSTDTC,
    time_imputation = "00:00:00"
  ) %>%
  derive_vars_dtm(
    new_vars_prefix = "AEN",
    dtc = EXENDTC,
    time_imputation = "00:00:00"
  ) %>%
  # Derive event ID and nominal relative time from first dose (NFRLT)
  mutate(
    EVID = 1,
    NFRLT = case_when(
      VISITDY == 1 ~ 0,
      TRUE ~ 24 * VISITDY
    )
  ) %>%
  # Set missing end dates to start date
  mutate(AENDTM = case_when(
    is.na(AENDTM) ~ ASTDTM,
    TRUE ~ AENDTM
  )) %>%
  # Derive dates from date/times
  derive_vars_dtm_to_dt(exprs(ASTDTM)) %>%
  derive_vars_dtm_to_dt(exprs(AENDTM))

Expand Dosing Records

Since there is a start date and end date for dosing records we need to expand the dosing records between the start date and end date using the function admiral::create_single_dose_dataset().

ex_exp <- ex_dates %>%
  create_single_dose_dataset(
    dose_freq = EXDOSFRQ,
    start_date = ASTDT,
    start_datetime = ASTDTM,
    end_date = AENDT,
    end_datetime = AENDTM,
    nominal_time = NFRLT,
    lookup_table = dose_freq_lookup,
    lookup_column = CDISC_VALUE,
    keep_source_vars = exprs(
      STUDYID, USUBJID, EVID, EXDOSFRQ, EXDOSFRM,
      NFRLT, EXDOSE, EXDOSU, EXTRT, ASTDT, ASTDTM, AENDT, AENDTM,
      VISIT, VISITNUM, VISITDY,
      TRT01A, TRT01P, DOMAIN, EXSEQ, !!!adsl_vars
    )
  ) %>%
  # Derive AVISIT based on nominal relative time
  # Derive AVISITN to nominal time in whole days using integer division
  # Define AVISIT based on nominal day
  mutate(
    AVISITN = NFRLT %/% 24 + 1,
    AVISIT = paste("Day", AVISITN),
    ADTM = ASTDTM,
    DRUG = EXTRT
  ) %>%
  # Derive dates and times from datetimes
  derive_vars_dtm_to_dt(exprs(ADTM)) %>%
  derive_vars_dtm_to_tm(exprs(ADTM)) %>%
  derive_vars_dtm_to_tm(exprs(ASTDTM)) %>%
  derive_vars_dtm_to_tm(exprs(AENDTM)) %>%
  derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))

Sample of Data

Find First Dose

In this section we will find the first dose for each subject and drug.

adpc_first_dose <- pc_dates %>%
  derive_vars_merged(
    dataset_add = ex_exp,
    filter_add = (EXDOSE > 0 & !is.na(ADTM)),
    new_vars = exprs(FANLDTM = ADTM),
    order = exprs(ADTM, EXSEQ),
    mode = "first",
    by_vars = exprs(STUDYID, USUBJID, DRUG)
  ) %>%
  filter(!is.na(FANLDTM)) %>%
  # Derive AVISIT based on nominal relative time
  # Derive AVISITN to nominal time in whole days using integer division
  # Define AVISIT based on nominal day
  mutate(
    AVISITN = NFRLT %/% 24 + 1,
    AVISIT = paste("Day", AVISITN),
  )

Sample of Data

Find Previous Dose and Next Dose

Use derive_vars_joined() to find the previous dose and the next dose.

adpc_prev <- adpc_first_dose %>%
  derive_vars_joined(
    dataset_add = ex_exp,
    by_vars = exprs(USUBJID),
    order = exprs(ADTM),
    new_vars = exprs(
      ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,
      AENDTM_prev = AENDTM
    ),
    join_vars = exprs(ADTM),
    join_type = "all",
    filter_add = NULL,
    filter_join = ADTM > ADTM.join,
    mode = "last",
    check_type = "none"
  )

adpc_next <- adpc_prev %>%
  derive_vars_joined(
    dataset_add = ex_exp,
    by_vars = exprs(USUBJID),
    order = exprs(ADTM),
    new_vars = exprs(
      ADTM_next = ADTM, EXDOSE_next = EXDOSE, AVISIT_next = AVISIT,
      AENDTM_next = AENDTM
    ),
    join_vars = exprs(ADTM),
    join_type = "all",
    filter_add = NULL,
    filter_join = ADTM <= ADTM.join,
    mode = "first",
    check_type = "none"
  )

Find Previous and Next Nominal Dose

Use the same method to find the previous and next nominal times.

adpc_nom_prev <- adpc_next %>%
  derive_vars_joined(
    dataset_add = ex_exp,
    by_vars = exprs(USUBJID),
    order = exprs(NFRLT),
    new_vars = exprs(NFRLT_prev = NFRLT),
    join_vars = exprs(NFRLT),
    join_type = "all",
    filter_add = NULL,
    filter_join = NFRLT > NFRLT.join,
    mode = "last",
    check_type = "none"
  )

adpc_nom_next <- adpc_nom_prev %>%
  derive_vars_joined(
    dataset_add = ex_exp,
    by_vars = exprs(USUBJID),
    order = exprs(NFRLT),
    new_vars = exprs(NFRLT_next = NFRLT),
    join_vars = exprs(NFRLT),
    join_type = "all",
    filter_add = NULL,
    filter_join = NFRLT <= NFRLT.join,
    mode = "first",
    check_type = "none"
  )

Sample of Data

Combine PC and EX Data

Combine PC and EX records and derive the additional relative time variables.

adpc_arrlt <- bind_rows(adpc_nom_next, ex_exp) %>%
  group_by(USUBJID, DRUG) %>%
  mutate(
    FANLDTM = min(FANLDTM, na.rm = TRUE),
    min_NFRLT = min(NFRLT_prev, na.rm = TRUE),
    maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID
  ) %>%
  arrange(USUBJID, ADTM) %>%
  ungroup() %>%
  filter(ADT <= maxdate) %>%
  # Derive Actual Relative Time from First Dose (AFRLT)
  derive_vars_duration(
    new_var = AFRLT,
    start_date = FANLDTM,
    end_date = ADTM,
    out_unit = "hours",
    floor_in = FALSE,
    add_one = FALSE
  ) %>%
  # Derive Actual Relative Time from Reference Dose (ARRLT)
  derive_vars_duration(
    new_var = ARRLT,
    start_date = ADTM_prev,
    end_date = ADTM,
    out_unit = "hours",
    floor_in = FALSE,
    add_one = FALSE
  ) %>%
  # Derive Actual Relative Time from Next Dose (AXRLT not kept)
  derive_vars_duration(
    new_var = AXRLT,
    start_date = ADTM_next,
    end_date = ADTM,
    out_unit = "hours",
    floor_in = FALSE,
    add_one = FALSE
  ) %>%
  mutate(
    ARRLT = case_when(
      EVID == 1 ~ 0,
      is.na(ARRLT) ~ AXRLT,
      TRUE ~ ARRLT
    ),
    # Derive Reference Dose Date
    PCRFTDTM = case_when(
      EVID == 1 ~ ADTM,
      is.na(ADTM_prev) ~ ADTM_next,
      TRUE ~ ADTM_prev
    )
  ) %>%
  # Derive dates and times from datetimes
  derive_vars_dtm_to_dt(exprs(FANLDTM)) %>%
  derive_vars_dtm_to_tm(exprs(FANLDTM)) %>%
  derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%
  derive_vars_dtm_to_tm(exprs(PCRFTDTM))

Derive Nominal Reference

For nominal relative times we calculate the nominal relative time to reference dose NRRLT.

# Derive Nominal Relative Time from Reference Dose (NRRLT)

adpc_nrrlt <- adpc_arrlt %>%
  mutate(
    NRRLT = case_when(
      EVID == 1 ~ 0,
      is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,
      TRUE ~ NFRLT - NFRLT_prev
    ),
    NXRLT = case_when(
      EVID == 1 ~ 0,
      TRUE ~ NFRLT - NFRLT_next
    )
  )

Sample of Data

Derive Analysis Variables

Here we derive the analysis variables such as AVAL and ATPTREF.

adpc_aval <- adpc_nrrlt %>%
  mutate(
    PARCAT1 = PCSPEC,
    ATPTN = case_when(
      EVID == 1 ~ 0,
      TRUE ~ PCTPTNUM
    ),
    ATPT = case_when(
      EVID == 1 ~ "Dose",
      TRUE ~ PCTPT
    ),
    ATPTREF = case_when(
      EVID == 1 ~ AVISIT,
      is.na(AVISIT_prev) ~ AVISIT_next,
      TRUE ~ AVISIT_prev
    ),
    # Derive baseline flag for pre-dose records
    ABLFL = case_when(
      ATPT == "Pre-dose" ~ "Y",
      TRUE ~ NA_character_
    ),
    # Derive BASETYPE
    BASETYPE = paste(ATPTREF, "Baseline"),

    # Derive Actual Dose
    DOSEA = case_when(
      EVID == 1 ~ EXDOSE,
      is.na(EXDOSE_prev) ~ EXDOSE_next,
      TRUE ~ EXDOSE_prev
    ),
    # Derive Planned Dose
    DOSEP = case_when(
      TRT01P == "Xanomeline High Dose" ~ 81,
      TRT01P == "Xanomeline Low Dose" ~ 54
    ),
    DOSEU = "mg",
  ) %>%
  # Derive relative time units
  mutate(
    FRLTU = "h",
    RRLTU = "h",
    # Derive PARAMCD
    PARAMCD = coalesce(PCTESTCD, "DOSE"),
    ALLOQ = PCLLOQ,
    # Derive AVAL
    AVAL = case_when(
      EVID == 1 ~ EXDOSE,
      PCSTRESC == "<BLQ" & NFRLT == 0 ~ 0,
      PCSTRESC == "<BLQ" & NFRLT > 0 ~ 0.5 * ALLOQ,
      TRUE ~ PCSTRESN
    ),
    AVALU = case_when(
      EVID == 1 ~ EXDOSU,
      TRUE ~ PCSTRESU
    ),
    AVALCAT1 = if_else(PCSTRESC == "<BLQ", PCSTRESC, prettyNum(signif(AVAL, digits = 3))),
  ) %>%
  # Add SRCSEQ
  mutate(
    SRCDOM = DOMAIN,
    SRCVAR = "SEQ",
    SRCSEQ = coalesce(PCSEQ, EXSEQ)
  )

Derive DTYPE Copy Records

The CDISC ADaM Implementation Guide for Non-compartmental Analysis uses duplicated records for analysis when a record needs to be used in more than one way. In this example the 24 hour post-dose record will also be used a the pre-dose record for the “Day 2” dose.

dtype <- adpc_aval %>%
  filter(NFRLT > 0 & NXRLT == 0 & EVID == 0 & !is.na(AVISIT_next)) %>%
  select(-PCRFTDT, -PCRFTTM) %>%
  # Re-derive variables in for DTYPE copy records
  mutate(
    ABLFL = NA_character_,
    ATPTREF = AVISIT_next,
    ARRLT = AXRLT,
    NRRLT = NXRLT,
    PCRFTDTM = ADTM_next,
    DOSEA = EXDOSE_next,
    BASETYPE = paste(AVISIT_next, "Baseline"),
    ATPT = "Pre-dose",
    ATPTN = NFRLT,
    ABLFL = "Y",
    DTYPE = "COPY"
  ) %>%
  derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%
  derive_vars_dtm_to_tm(exprs(PCRFTDTM))

Sample of Data

Combine Original and DTYPE Copy

Now the duplicated records are combined with the original records.

adpc_dtype <- bind_rows(adpc_aval, dtype) %>%
  arrange(STUDYID, USUBJID, BASETYPE, ADTM, NFRLT) %>%
  mutate(
    # Derive MRRLT, ANL01FL and ANL02FL
    MRRLT = if_else(ARRLT < 0, 0, ARRLT),
    ANL01FL = "Y",
    ANL02FL = if_else(is.na(DTYPE), "Y", NA_character_),
  )

Sample of Data

Derive BASE and CHG

# ---- Derive BASE and Calculate Change from Baseline ----

adpc_base <- adpc_dtype %>%
  derive_var_base(
    by_vars = exprs(STUDYID, USUBJID, PARAMCD, PARCAT1, BASETYPE),
    source_var = AVAL,
    new_var = BASE,
    filter = ABLFL == "Y"
  )

adpc_chg <- derive_var_chg(adpc_base)

Derive `PARAM` with `{metatools}`

Here we derive PARAM and PARAMN using create_var_from_codelist() from metatools.

# ---- Add ASEQ ----

adpc_aseq <- adpc_chg %>%
  # Calculate ASEQ
  derive_var_obs_number(
    new_var = ASEQ,
    by_vars = exprs(STUDYID, USUBJID),
    order = exprs(ADTM, BASETYPE, EVID, AVISITN, ATPTN, PARCAT1, DTYPE),
    check_type = "error"
  ) %>%
  # Derive PARAM and PARAMN using metatools
  create_var_from_codelist(metacore, input_var = PARAMCD, out_var = PARAM) %>%
  create_var_from_codelist(metacore, input_var = PARAMCD, out_var = PARAMN)

Derive Additional Baselines

Here we derive additional baseline values from VS for baseline height HTBL and weight WTBL and compute the body mass index (BMI) with compute_bmi().

#---- Derive additional baselines from VS ----

adpc_baselines <- adpc_aseq %>%
  derive_vars_merged(
    dataset_add = vs,
    filter_add = VSTESTCD == "HEIGHT",
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(HTBL = VSSTRESN, HTBLU = VSSTRESU)
  ) %>%
  derive_vars_merged(
    dataset_add = vs,
    filter_add = VSTESTCD == "WEIGHT" & VSBLFL == "Y",
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(WTBL = VSSTRESN, WTBLU = VSSTRESU)
  ) %>%
  mutate(
    BMIBL = compute_bmi(height = HTBL, weight = WTBL),
    BMIBLU = "kg/m^2"
  )

Sample of Data

Combine with ADSL

If needed, the other ADSL variables can now be added:

# ---- Add all ADSL variables ----

# Add all ADSL variables
adpc_prefinal <- adpc_baselines %>%
  derive_vars_merged(
    dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
    by_vars = exprs(STUDYID, USUBJID)
  )

Check Data With metacore and metatools

We use metacore objects with metatools functions to perform a number of checks on the data. We will drop variables not in the specs and make sure all the variables from the specs are included.

# Apply metadata and perform associated checks ----
adpc <- adpc_prefinal %>%
  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
  check_variables(metacore) %>% # Check all variables specified are present and no more
  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
  order_cols(metacore) %>% # Orders the columns according to the spec
  sort_by_key(metacore) # Sorts the rows by the sort keys

Apply Labels and Formats with xportr

Using xportr we check variable type, assign variable length, add variable labels, add variable formats, and save a transport file. At the end you could add a call to xportr::xportr_write() to produce the XPT file.

dir <- tempdir() # Change to whichever directory you want to save the dataset in

adpc_xpt <- adpc %>%
  xportr_type(metacore, domain = "ADPC") %>% # Coerce variable type to match spec
  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
  xportr_write(file.path(dir, "adpc.xpt")) # Write xpt v5 transport file

--- title: "ADPC" order: 2 --- ```{r setup script, include=FALSE, purl=FALSE} invisible_hook_purl <- function(before, options, ...) { knitr::hook_purl(before, options, ...) NULL } knitr::knit_hooks$set(purl = invisible_hook_purl) source("functions/print_df.R") ``` The Non-compartmental analysis (NCA) ADaM uses the CDISC Implementation Guide (<https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0>). This example presented uses underlying `EX` and `PC` domains where the `EX` and `PC` domains represent data as collected and the `ADPC` ADaM is output. For more details see the `{admiral}` [vignette](https://pharmaverse.github.io/admiral/articles/pk_adnca.html){target="_blank"}. ## First Load Packages First we will load the packages required for our project. We will use `{admiral}` for the creation of analysis data. `{admiral}` requires `{dplyr}`, `{lubridate}` and `{stringr}`. Find other `{admiral}` functions and related variables by searching [admiraldiscovery](<https://pharmaverse.github.io/admiraldiscovery/articles/reactable.html>). We will use `{metacore}` and `{metatools}` to store and manipulate metadata from our specifications. We will use `{xportr}` to perform checks on the final data and export to a transport file. The source SDTM data will come from the CDISC pilot study data stored in `{pharmaversesdtm}`. ```{r echo=TRUE, message=FALSE} #| label: Load Packages # Load Packages library(admiral) library(dplyr) library(lubridate) library(stringr) library(metacore) library(metatools) library(xportr) library(pharmaversesdtm) library(pharmaverseadam) library(reactable) ``` ## Next Load Specifications for Metacore We have saved our specifications in an Excel file and will load them into `{metacore}` with the `metacore::spec_to_metacore()` function. ```{r echo=TRUE} #| label: Load Specs #| warning: false # ---- Load Specs for Metacore ---- metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>% select_dataset("ADPC") ``` ## Load Source Datasets We will load are SDTM data from `{pharmaversesdtm}`. The main components of this will be exposure data from `EX` and pharmacokinetic concentration data from `PC`. We will use `ADSL` for baseline characteristics and we will derive additional baselines from vital signs `VS`. ```{r} #| label: Load Source # ---- Load source datasets ---- # Load PC, EX, VS, LB and ADSL ex <- pharmaversesdtm::ex pc <- pharmaversesdtm::pc vs <- pharmaversesdtm::vs adsl <- pharmaverseadam::adsl ex <- convert_blanks_to_na(ex) pc <- convert_blanks_to_na(pc) vs <- convert_blanks_to_na(vs) ``` ## Derivations ### Derive PC Dates Here we use `{admiral}` functions for working with dates and we will also create a nominal time from first dose `NFRLT` for `PC` data based on `PCTPTNUM`. ```{r} #| label: PC Dates # Get list of ADSL vars required for derivations adsl_vars <- exprs(TRTSDT, TRTSDTM, TRT01P, TRT01A) pc_dates <- pc %>% # Join ADSL with PC (need TRTSDT for ADY derivation) derive_vars_merged( dataset_add = adsl, new_vars = adsl_vars, by_vars = exprs(STUDYID, USUBJID) ) %>% # Derive analysis date/time # Impute missing time to 00:00:00 derive_vars_dtm( new_vars_prefix = "A", dtc = PCDTC, time_imputation = "00:00:00" ) %>% # Derive dates and times from date/times derive_vars_dtm_to_dt(exprs(ADTM)) %>% derive_vars_dtm_to_tm(exprs(ADTM)) %>% derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>% # Derive event ID and nominal relative time from first dose (NFRLT) mutate( EVID = 0, DRUG = PCTEST, NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(pc_dates %>% select(USUBJID, PCTEST, ADTM, VISIT, PCTPT, NFRLT)) ``` ### Get Dosing Information Here we also create nominal time from first dose `NFRLT` for `EX` data based on `VISITDY`. ```{r} #| label: Dosing ex_dates <- ex %>% derive_vars_merged( dataset_add = adsl, new_vars = adsl_vars, by_vars = exprs(STUDYID, USUBJID) ) %>% # Keep records with nonzero dose filter(EXDOSE > 0) %>% # Add time and set missing end date to start date # Impute missing time to 00:00:00 # Note all times are missing for dosing records in this example data # Derive Analysis Start and End Dates derive_vars_dtm( new_vars_prefix = "AST", dtc = EXSTDTC, time_imputation = "00:00:00" ) %>% derive_vars_dtm( new_vars_prefix = "AEN", dtc = EXENDTC, time_imputation = "00:00:00" ) %>% # Derive event ID and nominal relative time from first dose (NFRLT) mutate( EVID = 1, NFRLT = case_when( VISITDY == 1 ~ 0, TRUE ~ 24 * VISITDY ) ) %>% # Set missing end dates to start date mutate(AENDTM = case_when( is.na(AENDTM) ~ ASTDTM, TRUE ~ AENDTM )) %>% # Derive dates from date/times derive_vars_dtm_to_dt(exprs(ASTDTM)) %>% derive_vars_dtm_to_dt(exprs(AENDTM)) ``` ### Expand Dosing Records Since there is a start date and end date for dosing records we need to expand the dosing records between the start date and end date using the function `admiral::create_single_dose_dataset()`. ```{r} #| label: Expand ex_exp <- ex_dates %>% create_single_dose_dataset( dose_freq = EXDOSFRQ, start_date = ASTDT, start_datetime = ASTDTM, end_date = AENDT, end_datetime = AENDTM, nominal_time = NFRLT, lookup_table = dose_freq_lookup, lookup_column = CDISC_VALUE, keep_source_vars = exprs( STUDYID, USUBJID, EVID, EXDOSFRQ, EXDOSFRM, NFRLT, EXDOSE, EXDOSU, EXTRT, ASTDT, ASTDTM, AENDT, AENDTM, VISIT, VISITNUM, VISITDY, TRT01A, TRT01P, DOMAIN, EXSEQ, !!!adsl_vars ) ) %>% # Derive AVISIT based on nominal relative time # Derive AVISITN to nominal time in whole days using integer division # Define AVISIT based on nominal day mutate( AVISITN = NFRLT %/% 24 + 1, AVISIT = paste("Day", AVISITN), ADTM = ASTDTM, DRUG = EXTRT ) %>% # Derive dates and times from datetimes derive_vars_dtm_to_dt(exprs(ADTM)) %>% derive_vars_dtm_to_tm(exprs(ADTM)) %>% derive_vars_dtm_to_tm(exprs(ASTDTM)) %>% derive_vars_dtm_to_tm(exprs(AENDTM)) %>% derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df( ex_exp %>% select(USUBJID, DRUG, EXDOSFRQ, ASTDT, AVISIT, NFRLT) ) ``` ### Find First Dose In this section we will find the first dose for each subject and drug. ```{r} #| label: First Dose adpc_first_dose <- pc_dates %>% derive_vars_merged( dataset_add = ex_exp, filter_add = (EXDOSE > 0 & !is.na(ADTM)), new_vars = exprs(FANLDTM = ADTM), order = exprs(ADTM, EXSEQ), mode = "first", by_vars = exprs(STUDYID, USUBJID, DRUG) ) %>% filter(!is.na(FANLDTM)) %>% # Derive AVISIT based on nominal relative time # Derive AVISITN to nominal time in whole days using integer division # Define AVISIT based on nominal day mutate( AVISITN = NFRLT %/% 24 + 1, AVISIT = paste("Day", AVISITN), ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(adpc_first_dose %>% select(USUBJID, FANLDTM, NFRLT, ADTM, AVISITN, AVISIT, PCTPT)) ``` ### Find Previous Dose and Next Dose Use `derive_vars_joined()` to find the previous dose and the next dose. ```{r} #| label: Previous Dose and Next Dose adpc_prev <- adpc_first_dose %>% derive_vars_joined( dataset_add = ex_exp, by_vars = exprs(USUBJID), order = exprs(ADTM), new_vars = exprs( ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT, AENDTM_prev = AENDTM ), join_vars = exprs(ADTM), join_type = "all", filter_add = NULL, filter_join = ADTM > ADTM.join, mode = "last", check_type = "none" ) adpc_next <- adpc_prev %>% derive_vars_joined( dataset_add = ex_exp, by_vars = exprs(USUBJID), order = exprs(ADTM), new_vars = exprs( ADTM_next = ADTM, EXDOSE_next = EXDOSE, AVISIT_next = AVISIT, AENDTM_next = AENDTM ), join_vars = exprs(ADTM), join_type = "all", filter_add = NULL, filter_join = ADTM <= ADTM.join, mode = "first", check_type = "none" ) ``` ### Find Previous and Next Nominal Dose Use the same method to find the previous and next nominal times. ```{r} #| label: Previous Nominal Dose adpc_nom_prev <- adpc_next %>% derive_vars_joined( dataset_add = ex_exp, by_vars = exprs(USUBJID), order = exprs(NFRLT), new_vars = exprs(NFRLT_prev = NFRLT), join_vars = exprs(NFRLT), join_type = "all", filter_add = NULL, filter_join = NFRLT > NFRLT.join, mode = "last", check_type = "none" ) adpc_nom_next <- adpc_nom_prev %>% derive_vars_joined( dataset_add = ex_exp, by_vars = exprs(USUBJID), order = exprs(NFRLT), new_vars = exprs(NFRLT_next = NFRLT), join_vars = exprs(NFRLT), join_type = "all", filter_add = NULL, filter_join = NFRLT <= NFRLT.join, mode = "first", check_type = "none" ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(adpc_nom_prev %>% select(USUBJID, NFRLT, AVISIT, PCTPT, NFRLT_prev)) ``` ### Combine PC and EX Data Combine `PC` and `EX` records and derive the additional relative time variables. ```{r} #| label: Combine adpc_arrlt <- bind_rows(adpc_nom_next, ex_exp) %>% group_by(USUBJID, DRUG) %>% mutate( FANLDTM = min(FANLDTM, na.rm = TRUE), min_NFRLT = min(NFRLT_prev, na.rm = TRUE), maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID ) %>% arrange(USUBJID, ADTM) %>% ungroup() %>% filter(ADT <= maxdate) %>% # Derive Actual Relative Time from First Dose (AFRLT) derive_vars_duration( new_var = AFRLT, start_date = FANLDTM, end_date = ADTM, out_unit = "hours", floor_in = FALSE, add_one = FALSE ) %>% # Derive Actual Relative Time from Reference Dose (ARRLT) derive_vars_duration( new_var = ARRLT, start_date = ADTM_prev, end_date = ADTM, out_unit = "hours", floor_in = FALSE, add_one = FALSE ) %>% # Derive Actual Relative Time from Next Dose (AXRLT not kept) derive_vars_duration( new_var = AXRLT, start_date = ADTM_next, end_date = ADTM, out_unit = "hours", floor_in = FALSE, add_one = FALSE ) %>% mutate( ARRLT = case_when( EVID == 1 ~ 0, is.na(ARRLT) ~ AXRLT, TRUE ~ ARRLT ), # Derive Reference Dose Date PCRFTDTM = case_when( EVID == 1 ~ ADTM, is.na(ADTM_prev) ~ ADTM_next, TRUE ~ ADTM_prev ) ) %>% # Derive dates and times from datetimes derive_vars_dtm_to_dt(exprs(FANLDTM)) %>% derive_vars_dtm_to_tm(exprs(FANLDTM)) %>% derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>% derive_vars_dtm_to_tm(exprs(PCRFTDTM)) ``` ### Derive Nominal Reference For nominal relative times we calculate the nominal relative time to reference dose `NRRLT`. ```{r} #| label: Nominal Reference # Derive Nominal Relative Time from Reference Dose (NRRLT) adpc_nrrlt <- adpc_arrlt %>% mutate( NRRLT = case_when( EVID == 1 ~ 0, is.na(NFRLT_prev) ~ NFRLT - min_NFRLT, TRUE ~ NFRLT - NFRLT_prev ), NXRLT = case_when( EVID == 1 ~ 0, TRUE ~ NFRLT - NFRLT_next ) ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(adpc_nrrlt %>% select(USUBJID, NFRLT, NRRLT, AVISIT, PCTPT)) ``` ### Derive Analysis Variables Here we derive the analysis variables such as `AVAL` and `ATPTREF`. ```{r} #| label: Analysis Variables adpc_aval <- adpc_nrrlt %>% mutate( PARCAT1 = PCSPEC, ATPTN = case_when( EVID == 1 ~ 0, TRUE ~ PCTPTNUM ), ATPT = case_when( EVID == 1 ~ "Dose", TRUE ~ PCTPT ), ATPTREF = case_when( EVID == 1 ~ AVISIT, is.na(AVISIT_prev) ~ AVISIT_next, TRUE ~ AVISIT_prev ), # Derive baseline flag for pre-dose records ABLFL = case_when( ATPT == "Pre-dose" ~ "Y", TRUE ~ NA_character_ ), # Derive BASETYPE BASETYPE = paste(ATPTREF, "Baseline"), # Derive Actual Dose DOSEA = case_when( EVID == 1 ~ EXDOSE, is.na(EXDOSE_prev) ~ EXDOSE_next, TRUE ~ EXDOSE_prev ), # Derive Planned Dose DOSEP = case_when( TRT01P == "Xanomeline High Dose" ~ 81, TRT01P == "Xanomeline Low Dose" ~ 54 ), DOSEU = "mg", ) %>% # Derive relative time units mutate( FRLTU = "h", RRLTU = "h", # Derive PARAMCD PARAMCD = coalesce(PCTESTCD, "DOSE"), ALLOQ = PCLLOQ, # Derive AVAL AVAL = case_when( EVID == 1 ~ EXDOSE, PCSTRESC == "<BLQ" & NFRLT == 0 ~ 0, PCSTRESC == "<BLQ" & NFRLT > 0 ~ 0.5 * ALLOQ, TRUE ~ PCSTRESN ), AVALU = case_when( EVID == 1 ~ EXDOSU, TRUE ~ PCSTRESU ), AVALCAT1 = if_else(PCSTRESC == "<BLQ", PCSTRESC, prettyNum(signif(AVAL, digits = 3))), ) %>% # Add SRCSEQ mutate( SRCDOM = DOMAIN, SRCVAR = "SEQ", SRCSEQ = coalesce(PCSEQ, EXSEQ) ) ``` ### Derive DTYPE Copy Records The CDISC ADaM Implementation Guide for Non-compartmental Analysis uses duplicated records for analysis when a record needs to be used in more than one way. In this example the 24 hour post-dose record will also be used a the pre-dose record for the "Day 2" dose. ```{r} #| label: DTYPE dtype <- adpc_aval %>% filter(NFRLT > 0 & NXRLT == 0 & EVID == 0 & !is.na(AVISIT_next)) %>% select(-PCRFTDT, -PCRFTTM) %>% # Re-derive variables in for DTYPE copy records mutate( ABLFL = NA_character_, ATPTREF = AVISIT_next, ARRLT = AXRLT, NRRLT = NXRLT, PCRFTDTM = ADTM_next, DOSEA = EXDOSE_next, BASETYPE = paste(AVISIT_next, "Baseline"), ATPT = "Pre-dose", ATPTN = NFRLT, ABLFL = "Y", DTYPE = "COPY" ) %>% derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>% derive_vars_dtm_to_tm(exprs(PCRFTDTM)) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(dtype %>% select(USUBJID, DTYPE, BASETYPE, ATPT, NFRLT, NRRLT, AFRLT, ARRLT)) ``` ### Combine Original and DTYPE Copy Now the duplicated records are combined with the original records. ```{r} #| label: Combine DTYPE adpc_dtype <- bind_rows(adpc_aval, dtype) %>% arrange(STUDYID, USUBJID, BASETYPE, ADTM, NFRLT) %>% mutate( # Derive MRRLT, ANL01FL and ANL02FL MRRLT = if_else(ARRLT < 0, 0, ARRLT), ANL01FL = "Y", ANL02FL = if_else(is.na(DTYPE), "Y", NA_character_), ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(adpc_dtype %>% select(USUBJID, DTYPE, BASETYPE, ATPT, NFRLT, NRRLT, AFRLT, ARRLT)) ``` ### Derive BASE and CHG ```{r} #| label: BASE # ---- Derive BASE and Calculate Change from Baseline ---- adpc_base <- adpc_dtype %>% derive_var_base( by_vars = exprs(STUDYID, USUBJID, PARAMCD, PARCAT1, BASETYPE), source_var = AVAL, new_var = BASE, filter = ABLFL == "Y" ) adpc_chg <- derive_var_chg(adpc_base) ``` ### Derive `PARAM` with `{metatools}` Here we derive `PARAM` and `PARAMN` using `create_var_from_codelist()` from `{metatools}`. ```{r} #| label: ASEQ # ---- Add ASEQ ---- adpc_aseq <- adpc_chg %>% # Calculate ASEQ derive_var_obs_number( new_var = ASEQ, by_vars = exprs(STUDYID, USUBJID), order = exprs(ADTM, BASETYPE, EVID, AVISITN, ATPTN, PARCAT1, DTYPE), check_type = "error" ) %>% # Derive PARAM and PARAMN using metatools create_var_from_codelist(metacore, input_var = PARAMCD, out_var = PARAM) %>% create_var_from_codelist(metacore, input_var = PARAMCD, out_var = PARAMN) ``` ### Derive Additional Baselines Here we derive additional baseline values from `VS` for baseline height `HTBL` and weight `WTBL` and compute the body mass index (BMI) with `compute_bmi()`. ```{r} #| label: Baselines #---- Derive additional baselines from VS ---- adpc_baselines <- adpc_aseq %>% derive_vars_merged( dataset_add = vs, filter_add = VSTESTCD == "HEIGHT", by_vars = exprs(STUDYID, USUBJID), new_vars = exprs(HTBL = VSSTRESN, HTBLU = VSSTRESU) ) %>% derive_vars_merged( dataset_add = vs, filter_add = VSTESTCD == "WEIGHT" & VSBLFL == "Y", by_vars = exprs(STUDYID, USUBJID), new_vars = exprs(WTBL = VSSTRESN, WTBLU = VSSTRESU) ) %>% mutate( BMIBL = compute_bmi(height = HTBL, weight = WTBL), BMIBLU = "kg/m^2" ) ``` ```{r eval=TRUE, echo=FALSE, purl=FALSE} print_df(adpc_baselines %>% select(USUBJID, HTBL, HTBLU, WTBL, WTBLU, BMIBL, BMIBLU, BASETYPE, ATPT, AVAL)) ``` ### Combine with ADSL If needed, the other `ADSL` variables can now be added: ```{r} #| label: Combine with ADSL # ---- Add all ADSL variables ---- # Add all ADSL variables adpc_prefinal <- adpc_baselines %>% derive_vars_merged( dataset_add = select(adsl, !!!negate_vars(adsl_vars)), by_vars = exprs(STUDYID, USUBJID) ) ``` ## Check Data With metacore and metatools We use `{metacore}` objects with `{metatools}` functions to perform a number of checks on the data. We will drop variables not in the specs and make sure all the variables from the specs are included. ```{r} #| label: Metacore #| warning: false # Apply metadata and perform associated checks ---- adpc <- adpc_prefinal %>% drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs check_variables(metacore) %>% # Check all variables specified are present and no more check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT order_cols(metacore) %>% # Orders the columns according to the spec sort_by_key(metacore) # Sorts the rows by the sort keys ``` ## Apply Labels and Formats with xportr Using `{xportr}` we check variable type, assign variable length, add variable labels, add variable formats, and save a transport file. At the end you could add a call to `xportr::xportr_write()` to produce the XPT file. ```{r} #| label: xportr #| warning: false dir <- tempdir() # Change to whichever directory you want to save the dataset in adpc_xpt <- adpc %>% xportr_type(metacore, domain = "ADPC") %>% # Coerce variable type to match spec xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata xportr_label(metacore) %>% # Assigns variable label from metacore specifications xportr_format(metacore) %>% # Assigns variable format from metacore specifications xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications xportr_write(file.path(dir, "adpc.xpt")) # Write xpt v5 transport file ```

First Load Packages

Next Load Specifications for Metacore

Load Source Datasets

Derivations

Derive PC Dates

Sample of Data

Get Dosing Information

Expand Dosing Records

Sample of Data

Find First Dose

Sample of Data

Find Previous Dose and Next Dose

Find Previous and Next Nominal Dose

Sample of Data

Combine PC and EX Data

Derive Nominal Reference

Sample of Data

Derive Analysis Variables

Derive DTYPE Copy Records

Sample of Data

Combine Original and DTYPE Copy

Sample of Data

Derive BASE and CHG

Derive PARAM with {metatools}

Derive Additional Baselines

Sample of Data

Combine with ADSL

Check Data With metacore and metatools

Apply Labels and Formats with xportr

Derive `PARAM` with `{metatools}`