Case study: data-cut review on public ADaM data

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This case study runs the full trialdiff pipeline on the public pharmaverseadam datasets: two data cuts are compared, changes are classified, lineage is generated from metadata and an output registry, downstream impact is assessed, and a review report is produced. No proprietary data is used.

The two data cuts

We use ADSL and a subset of ADLB (three laboratory parameters) and build a later cut that introduces the kinds of changes seen in practice: two new subjects, a treatment-assignment correction, a corrected laboratory value and a value that becomes missing.

params <- c("ALT", "AST", "CREAT")
adsl_full <- pharmaverseadam::adsl
adlb_full <- subset(pharmaverseadam::adlb, PARAMCD %in% params)

subjects <- unique(as.character(adsl_full$USUBJID))
new_subjects <- tail(subjects, 2)

adsl_cut1 <- adsl_full[!adsl_full$USUBJID %in% new_subjects, ]
adlb_cut1 <- adlb_full[!adlb_full$USUBJID %in% new_subjects, ]
adsl_cut2 <- adsl_full
adlb_cut2 <- adlb_full

trt_subject <- adsl_cut2$USUBJID[which(adsl_cut2$TRT01P == "Placebo")[1]]
for (v in c("TRT01P", "TRT01A")) {
  adsl_cut2[[v]][adsl_cut2$USUBJID == trt_subject] <- "Xanomeline Low Dose"
}
for (v in c("TRT01P", "TRTP")) {
  adlb_cut2[[v]][adlb_cut2$USUBJID == trt_subject] <- "Xanomeline Low Dose"
}

i <- which(adlb_cut2$USUBJID == trt_subject & adlb_cut2$PARAMCD == "ALT" &
             adlb_cut2$AVISIT == "Week 4")[1]
adlb_cut2$AVAL[i] <- adlb_cut2$AVAL[i] + 7
adlb_cut2$CHG[i] <- adlb_cut2$AVAL[i] - adlb_cut2$BASE[i]

j <- which(adlb_cut2$PARAMCD == "AST" & adlb_cut2$AVISIT == "Week 2")[1]
adlb_cut2$AVAL[j] <- NA_real_
adlb_cut2$CHG[j] <- NA_real_

c(adsl = nrow(adsl_cut2) - nrow(adsl_cut1), adlb = nrow(adlb_cut2) - nrow(adlb_cut1))
#> adsl adlb 
#>    2   51

Lineage from metadata plus a registry

The data and derived-variable portion of the lineage comes from a small metadata specification (the same shape as a metacore object). Analyses and outputs are declared with output_registry() and grafted on with overrides.

metadata <- list(
  ds_spec = data.frame(
    dataset = c("ADSL", "ADLB"),
    label = c("Subject-Level Analysis", "Laboratory Analysis")
  ),
  ds_vars = data.frame(
    dataset = c("ADSL", "ADSL", "ADSL", "ADLB", "ADLB", "ADLB", "ADLB", "ADLB"),
    variable = c("USUBJID", "TRT01P", "SAFFL", "USUBJID", "PARAMCD",
                 "TRT01P", "AVAL", "CHG")
  ),
  value_spec = data.frame(
    dataset = c("ADSL", "ADLB", "ADLB", "ADLB", "ADLB"),
    variable = c("TRT01P", "TRT01P", "AVAL", "BASE", "CHG"),
    derivation_id = c("MT.ADSL.TRT01P", "MT.ADLB.TRT01P", "MT.ADLB.AVAL",
                      "MT.ADLB.BASE", "MT.ADLB.CHG"),
    where = c(NA, NA, "PARAMCD == 'ALT'", NA, NA)
  ),
  derivations = data.frame(
    derivation_id = c("MT.ADSL.TRT01P", "MT.ADLB.TRT01P", "MT.ADLB.AVAL",
                      "MT.ADLB.BASE", "MT.ADLB.CHG"),
    derivation = c("DM.ARM", "ADSL.TRT01P", "LB.LBSTRESN", "ADLB.AVAL",
                   "AVAL - BASE")
  )
)

registry <- output_registry(
  td_output("Lab_Summary_By_Treatment",
            depends_on = c("ADLB.TRT01P", "ADLB.AVAL"),
            type = "analysis", relationship = "summarises"),
  td_output("MMRM", depends_on = c("ADLB.AVAL", "ADLB.CHG")),
  td_output("Table_14_2_1", depends_on = "Lab_Summary_By_Treatment",
            type = "output"),
  td_output("Table_14_2_2", depends_on = "MMRM", type = "output")
)

lineage <- lineage_from_metadata(metadata, overrides = registry)
lineage
#> 
#> ── trialdiff lineage ───────────────────────────────────────────────────────────
#> 12 edges, 17 nodes
#> analysis: 2
#> dataset: 2
#> output: 2
#> variable: 11

The generated graph reaches from the raw source through derived variables to analyses and TLFs, and every edge keeps its provenance:

lineage_provenance(lineage)[, c("from", "to", "relationship", "source")]
#> # A tibble: 12 × 4
#>    from                     to                       relationship source        
#>    <chr>                    <chr>                    <chr>        <chr>         
#>  1 DM.ARM                   ADSL.TRT01P              derives      metacore:deri…
#>  2 ADSL.TRT01P              ADLB.TRT01P              derives      metacore:deri…
#>  3 LB.LBSTRESN              ADLB.AVAL                derives      metacore:deri…
#>  4 ADLB.PARAMCD             ADLB.AVAL                filters      metacore:where
#>  5 ADLB.AVAL                ADLB.BASE                derives      metacore:deri…
#>  6 ADLB.AVAL                ADLB.CHG                 derives      metacore:deri…
#>  7 ADLB.TRT01P              Lab_Summary_By_Treatment summarises   registry      
#>  8 ADLB.AVAL                Lab_Summary_By_Treatment summarises   registry      
#>  9 ADLB.AVAL                MMRM                     models       registry      
#> 10 ADLB.CHG                 MMRM                     models       registry      
#> 11 Lab_Summary_By_Treatment Table_14_2_1             reports      registry      
#> 12 MMRM                     Table_14_2_2             reports      registry

Compare and classify

adsl_diff <- compare_cut(adsl_cut1, adsl_cut2, by = "USUBJID",
                         dataset = "ADSL") |>
  classify_changes()
adlb_diff <- compare_cut(adlb_cut1, adlb_cut2,
                         by = c("USUBJID", "PARAMCD", "AVISIT"),
                         dataset = "ADLB") |>
  classify_changes()

knitr::kable(table(adsl_diff$register$category_label),
             col.names = c("Category", "ADSL"))

Category	ADSL
New subject	2
Treatment-assignment change	2

knitr::kable(table(adlb_diff$register$category_label),
             col.names = c("Category", "ADLB"))

Category	ADLB
Derived-variable change	2
New subject	51
Non-missing to missing	2
Treatment-assignment change	78

Assess downstream impact

adlb_impact <- assess_impact(adlb_diff, lineage)
adlb_impact$impacts[, c("node", "node_type", "level", "depth", "requires_rerun")]
#> # A tibble: 7 × 5
#>   node                     node_type level                depth requires_rerun
#>   <chr>                    <chr>     <chr>                <int> <lgl>         
#> 1 ADLB.BASE                variable  definitely_affected      1 FALSE         
#> 2 ADLB.CHG                 variable  definitely_affected      1 FALSE         
#> 3 ADLB.AVAL                variable  potentially_affected     1 FALSE         
#> 4 Lab_Summary_By_Treatment analysis  potentially_affected     1 TRUE          
#> 5 MMRM                     analysis  potentially_affected     1 TRUE          
#> 6 Table_14_2_1             output    potentially_affected     2 TRUE          
#> 7 Table_14_2_2             output    potentially_affected     2 TRUE

A treatment-assignment change flows to ADLB.TRT01P, the by-treatment summary and the MMRM, and every analysis or output is flagged for review or rerun. No statistical impact is claimed.

Review report

report <- report_diff(adsl_diff,
                      impact = assess_impact(adsl_diff, lineage),
                      output = "list")
report$data$review_items
#> # A tibble: 3 × 3
#>   item           detail                                                    owner
#>   <chr>          <chr>                                                     <chr>
#> 1 Rerun required Lab_Summary_By_Treatment (potentially_affected) - Potent… Stat…
#> 2 Rerun required Table_14_2_1 (potentially_affected) - Potentially affect… Stat…
#> 3 Lineage gap    1 changed node(s) have no declared lineage: ADSL.TRT01A.  Prog…

Writing report_diff(..., output = "cut-review.html") produces a self-contained HTML report, and as_json() produces machine-readable output for automated QC pipelines.

What this demonstrates

Deterministic comparison of two real data cuts, with clinical classification.
Lineage generated from metadata, extended with an analysis/output registry.
Transparent impact assessment that reaches TLFs and never claims statistical significance.
A single workflow: compare_cut() -> classify_changes() -> lineage_from_metadata() + output_registry() -> assess_impact() -> report_diff().

Limitations

The metadata above is a compact illustration; a real study would load it from Define-XML with metacore::define_to_metacore().
Analysis/output dependencies are not described by data metadata and must be supplied by the study team (here, via output_registry()).
Impact is a review signal. Confirming whether a summary or model result actually changes requires rerunning it.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.