ARD program structure

Step 1: Apply “Analysis Set” to ADaM(s)

This step applies the Analysis Set assigned to the output (e.g. Safety Population) to the ADaM dataset(s). In the case where the “big N” count is based on another dataset (like ADSL) than the main ADaM (e.g. ADAE), two separate datasets are created for downstream use in subsequent analyses. Example:

overlap <- intersect(names(ADSL), names(ADAE))
overlapfin <- setdiff(overlap, "USUBJID")

df_pop <- dplyr::filter(
  ADSL,
  SAFFL == "Y"
) |>
  merge(ADAE |> dplyr::select(-dplyr::all_of(overlapfin)),
    by = "USUBJID",
    all = FALSE
  )

df_poptot <- dplyr::filter(
  ADSL,
  SAFFL == "Y"
)

Note: this is only done once for the first Analysis, and assigned by subsequent analyses, since the dataset(s) remain the same for the remainder of the program’s analyses.

Step 2: Apply “Data Subset”

Based on the resulting dataset from step 1, further data subsetting is applied which is relevant to the current analysis (e.g. filtering for serious, treatment-related Adverse Events). If no data subsetting is required for the analysis, a simple assignment of the previous dataset is done with no ‘filter’ statement. This step has a convention of starting the dataframe name with “df2”, followed by the AnalysisId.

df2_An07_03_SerTEAE_Summ_ByTrt <- df_pop |>
  dplyr::filter(TRTEMFL == "Y" & AESER == "Y")

Step 3: Apply “Method”

This step takes the subsetted dataset, and applies the required AnalysisMethod (e.g. counting subjects by treatment and a group, like RACE). As explained in the vignette for using cards and cardx, functions from these packages are applied to handle the statistical operations for the analysis. Typically, there would be some pre-work done on the dataset before passing it to a cards or cardx function. When the function is applied, the result is an analysis-level ARD. At the end of this step, record-level metadata from the ARS model is also merged to the ARD, to ensure the ability to trace each result back to ARS metadata. See example below:

# intermediate step: Prepare Denominator Dataset for `cards` function
denom_dataset <- df2_An01_05_SAF_Summ_ByTrt |>
  dplyr::select(TRT01A)

# intermediate step: Prepare input dataset for `cards` function
in_data <- df2_An03_05_Race_Summ_ByTrt |>
  dplyr::distinct(TRT01A, RACE, USUBJID) |>
  dplyr::mutate(dummy = "dummyvar")

# calculate subject counts and % (based on big N) grouped by treatment and race
df3_An03_05_Race_Summ_ByTrt <- cards::ard_tabulate(
  data = in_data,
  by = c("TRT01A", "RACE"),
  variables = "dummy",
  denominator = denom_dataset
)

# select relevant statistics as defined by the Method, and assign operation Ids
df3_An03_05_Race_Summ_ByTrt <- df3_An03_05_Race_Summ_ByTrt |>
  dplyr::filter(stat_name %in% c("n", "p")) |>
  dplyr::mutate(operationid = dplyr::case_when(
    stat_name == "n" ~ "Mth01_1_n",
    stat_name == "p" ~ "Mth01_2_pct"
  ))

# stamp CDISC ARD traceability metadata so each result traces back to the ARS definition
df3_An03_05_Race_Summ_ByTrt <- df3_An03_05_Race_Summ_ByTrt |>
  dplyr::mutate(
    AnalysisId = "An03_05_Race_Summ_ByTrt",
    MethodId = "Mth01",
    OutputId = "Out14-1-1",
    # the grouping this column belongs to (here: the treatment-arm grouping)
    group1_groupingId = "AnlsGrouping_01_Trt",
    # for pre-defined groups, map each observed level to its ARS group id
    group1_groupId = dplyr::case_when(
      as.character(group1_level) == "Placebo"              ~ "AnlsGrouping_01_Trt_1",
      as.character(group1_level) == "Xanomeline Low Dose"  ~ "AnlsGrouping_01_Trt_2",
      as.character(group1_level) == "Xanomeline High Dose" ~ "AnlsGrouping_01_Trt_3"
    )
  )

This example uses the current cards function name ard_tabulate() (formerly ard_categorical()); see the using cards and cardx vignette for the full list of renames.

The final mutate() is where siera stamps the CDISC ARD traceability columns onto every row. For each grouping applied to the analysis you will see a group[n]_groupingId (which grouping the column belongs to) plus one of:

• group[n]_groupId - for pre-defined groups (groups listed in the metadata, like treatment arms), mapped from group[n]_level via case_when(), as above; or
• group[n]_groupValue - for data-driven groupings (dataDriven: true, where categories such as cause of death are discovered from the ADaM data at run time), capturing the discovered value directly.

See the Concepts and conventions vignette for the full picture of these traceability columns.