Author: Dr Nicolas Smoll, SCPHU, Sunshine Coast Hospital and Health Service
Date: 2026-07-10

# Linked Cohort Analysis: Linking Case Records to Vaccination History

### A complete starling workflow from preflight to perch

#### Dr Nicolas Smoll, SCPHU, Sunshine Coast Hospital and Health Service

#### 2026-07-10

## Overview

This vignette demonstrates the complete `starling` probabilistic record linkage workflow: from pre-linkage data quality assessment through blocking variable construction, threshold sensitivity analysis, linkage, and post-linkage validation. The scenario mirrors a routine SCPHU task: linking a notifiable disease linelist (EDIS extracts) to the Australian Immunisation Register (AIR) to determine vaccination status at the time of disease onset.

The datasets used (`cases_notifiable` and `vax_air`) are synthetic — no real person data. They include deliberate data quality issues (name typos, corrupted Medicare numbers) to demonstrate how the starling toolkit handles real-world messiness.

    library(starling)

    data(cases_notifiable)
    data(vax_air)

    cat("Cases linelist:      ", nrow(cases_notifiable), "records\n")
    cat("Vaccination register:", nrow(vax_air),           "records\n")
    cat("True matches (known):", sum(!is.na(cases_notifiable$true_link_id)), "\n")

## Step 1: Pre-linkage audit with `preflight()`

Before generating a single candidate pair, `preflight()` runs a structured battery of checks across both datasets: completeness of linkage variables, duplicate identifiers, date plausibility, Medicare validity, name field quality, and factor-level consistency.

    audit <- preflight(
      data1        = cases_notifiable,
      data2        = vax_air,
      linkage_vars = c("lettername1", "lettername2", "dob", "medicare10"),
      id_col1      = "id_var",
      id_col2      = "id_var",
      date_cols    = c("dob", "onset_date"),
      medicare_col = "medicare10"
    )

The audit flags include: - Any linkage variables with missingness above 10% - Duplicate ID values in either dataset - Medicare numbers that fail the Modulus 10 checksum - Date values before 1900 or after today

## Step 2: Medicare checksum validation with `check_medicare()`

The `preflight()` report includes Medicare validity, but `check_medicare()` can also be called standalone for a more detailed summary and to add the validation flag column for downstream use.

    # Validate cases
    cases_checked <- check_medicare(cases_notifiable,
                                    medicare_col = "medicare10",
                                    output_col   = "medicare_valid",
                                    verbose      = TRUE)

    # Confirm AIR Medicare numbers are all valid
    vax_checked <- check_medicare(vax_air,
                                  medicare_col = "medicare10",
                                  output_col   = "medicare_valid",
                                  verbose      = TRUE)

    # Replace corrupted Medicare numbers with NA before linkage
    # so they don't negatively score a true match
    cases_checked$medicare10 <- ifelse(
      cases_checked$medicare_valid == 1L,
      cases_checked$medicare10,
      NA_character_
    )

The cases dataset has ~10% corrupted Medicare numbers by design. Setting those to `NA` before linkage is better than passing an invalid number, because the EM algorithm treats `NA` as “not observed” (no contribution to the score, positive or negative), whereas an invalid number that happens to match a wrong AIR record would add spurious positive weight.

## Step 3: Blocking variable construction with `flock()`

`flock()` creates blocking keys that partition both datasets into candidate comparison groups. `murmuration()` only compares pairs within the same block, making the search tractable for large datasets.

    # Extract birth year for composite blocking
    cases_blocked <- flock(cases_checked,
                           block1_vars    = "gender",
                           block2_vars    = "gender",
                           block3_vars    = "postcode",
                           birth_year_col = "dob")

    vax_blocked   <- flock(vax_checked,
                           block1_vars    = "gender",
                           block2_vars    = "gender",
                           block3_vars    = "postcode",
                           birth_year_col = "dob")

    # Summary of blocking key distributions
    cat("block1 (gender) — unique values in cases:",
        dplyr::n_distinct(cases_blocked$block1), "\n")
    cat("block3 (postcode) — unique values in cases:",
        dplyr::n_distinct(cases_blocked$block3), "\n")

For this small synthetic dataset we use `block1` (gender only). For large production datasets (> 100 000 records), use multi-pass blocking: run `murmuration()` separately with `block1` and `block3`, then union the results.

## Step 4: Threshold sensitivity analysis with `perch()`

Before committing to a threshold, we can use `perch()` standalone to understand the score landscape. Alternatively, `murmuration(perch_before_linking = TRUE)` calls `perch()` automatically mid-linkage after the EM model fits.

    # This would require running the EM model first —
    # see the murmuration() call below which does this in one step.
    # For standalone use on a pre-scored pairs object:

    # pairs <- reclin2::pair_blocking(cases_blocked, vax_blocked, "block1")
    # reclin2::compare_pairs(pairs,
    #   on = c("lettername1", "lettername2", "dob", "medicare10"),
    #   default_comparator = reclin2::jaro_winkler(0.9), inplace = TRUE)
    # m          <- reclin2::problink_em(
    #   ~ lettername1 + lettername2 + dob + medicare10, data = pairs)
    # pairs_pred <- predict(m, pairs = pairs, add = TRUE)
    #
    # perch(pairs_pred, n_records_df1 = nrow(cases_blocked),
    #       thresholds = seq(8, 25, by = 1))

The threshold guidance from Australian linkage authorities:

Range | Source | Meaning
--- | --- | ---
10–20 | AIHW / WA Data Linkage Unit | Clerical review zone
15–20 | PHRN | Operational target for <0.5% false-match rate
17 | starling default | Balanced for routine surveillance

## Step 5: Probabilistic linkage with `murmuration()`

`murmuration()` runs the complete Fellegi-Sunter EM linkage pipeline in one call. We use `perch_before_linking = TRUE` to inspect the score distribution before the threshold is applied.

    linked <- murmuration(
      df1                  = cases_blocked,
      df2                  = vax_blocked,
      linkage_type         = "v2c",
      event_date           = "onset_date",
      id_var               = "id_var",
      blocking_var         = "block1",
      compare_vars         = c("lettername1", "lettername2", "dob", "medicare10"),
      threshold_value      = 17,
      perch_before_linking = FALSE,   # set TRUE in interactive sessions to inspect
      days_allowed_before_event = 14,
      clean_eggs           = TRUE
    )

    cat("Linked records:      ", nrow(linked), "\n")
    cat("With vaccination:    ", sum(!is.na(linked$vax_date_1)), "\n")
    cat("Without vaccination: ", sum( is.na(linked$vax_date_1)), "\n")

## Step 6: Visualise the score distribution with `murmuration_plot()`

Even if `perch_before_linking = FALSE` during the linkage call, we can still inspect the weight distribution afterwards by accessing the `weights` column on the linked output.

    # The linked output retains the weights column when clean_eggs = TRUE
    # For the visualisation, we use the weights from the linked data frame
    if ("weights" %in% names(linked)) {
      murmuration_plot(linked, threshold = 17, show_density = FALSE,
                       palette = "sch")
    }

## Step 7: Post-linkage validation

Because `cases_notifiable` contains `true_link_id` (the ground-truth match identifier), we can compute recall and precision on the synthetic data. This step is only possible with synthetic data — in production, post-linkage validation requires a clerical review sample.

    # Recall: proportion of true matches recovered
    true_positives <- sum(
      !is.na(linked$true_link_id) &
      !is.na(linked$id_var_df2) &
      linked$true_link_id == linked$id_var_df2,
      na.rm = TRUE
    )
    total_true_matches <- sum(!is.na(cases_notifiable$true_link_id))
    recall <- true_positives / total_true_matches

    # Precision: proportion of accepted links that are true matches
    total_links <- sum(!is.na(linked$id_var_df2))
    precision   <- true_positives / total_links

    cat(sprintf("Recall:    %.1f%%  (%d / %d true matches recovered)\n",
                recall * 100, true_positives, total_true_matches))
    cat(sprintf("Precision: %.1f%%  (%d / %d links are true matches)\n",
                precision * 100, true_positives, total_links))
    cat(sprintf("F1 score:  %.3f\n",
                2 * precision * recall / (precision + recall)))

## Summary: the complete starling workflow

    library(starling)
    data(cases_notifiable); data(vax_air)

    # 1. Pre-linkage audit
    preflight(cases_notifiable, vax_air,
              linkage_vars = c("lettername1", "lettername2", "dob", "medicare10"),
              medicare_col = "medicare10")

    # 2. Medicare validation — replace invalid numbers with NA
    cases <- check_medicare(cases_notifiable)
    cases$medicare10 <- ifelse(cases$medicare_valid == 1L, cases$medicare10, NA_character_)

    # 3. Blocking variables
    cases <- flock(cases, block1_vars = "gender", birth_year_col = "dob")
    vax   <- flock(vax_air, block1_vars = "gender", birth_year_col = "dob")

    # 4. Link (perch_before_linking = TRUE in interactive sessions)
    linked <- murmuration(cases, vax,
      linkage_type    = "v2c",
      event_date      = "onset_date",
      id_var          = "id_var",
      blocking_var    = "block1",
      compare_vars    = c("lettername1", "lettername2", "dob", "medicare10"),
      threshold_value = 17)

    # 5. Pass to mudnester or bowerbird for downstream analysis

## Session information

    sessionInfo()
    #> R version 4.5.2 (2025-10-31 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows 11 x64 (build 26100)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C                       LC_CTYPE=English_Australia.utf8
    #> [3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
    #> [5] LC_TIME=English_Australia.utf8
    #>
    #> time zone: Australia/Brisbane
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] digest_0.6.39     R6_2.6.1          fastmap_1.2.0     xfun_0.56
    #>  [5] cachem_1.1.0      knitr_1.51        htmltools_0.5.9   rmarkdown_2.31
    #>  [9] lifecycle_1.0.5   cli_3.6.5         sass_0.4.10       jquerylib_0.1.4
    #> [13] compiler_4.5.2    rstudioapi_0.18.0 tools_4.5.2       evaluate_1.0.5
    #> [17] bslib_0.10.0      yaml_2.3.12       otel_0.2.0        jsonlite_2.0.0
    #> [21] rlang_1.2.0