{
  "abstract": "Background\n\nPatient-reported outcome measures (PROMs) are increasingly integrated into clinical quality registries. Registry-based PROM data frequently exhibit structural non-response, including patients who never return PROMs (\"never-responders\"), and longitudinal attrition, making missing-not-at-random (MNAR) mechanisms plausible. Although MNAR sensitivity methods such as delta adjustment and tipping-point analysis are methodologically well-described, their uptake in registry-PROM practice is unknown.Methods\n\nA structured search of PubMed (Nordic-specific terms), PubMed (global terms), Embase and CINAHL was conducted (2018-2024), yielding 425 records. After deduplication and screening, 61 registry-based observational PROM studies with explicit missing-data methods discussion were included as the primary analytic sample (17 Nordic, 44 global). The audit was conducted as a methodological scoping review and reported in accordance with the PRISMA extension for scoping reviews (PRISMA-ScR). A three-step pragmatic workflow is proposed and demonstrated: (1) non-response diagnostics separating never-responders from dropout, with standardized mean differences (SMD); (2) inverse probability weighting (IPW) as a default primary analysis strategy; and (3) MNAR sensitivity via delta adjustment with explicit tipping-point identification and a simple robustness classification. The workflow is demonstrated in a simulated registry-like cohort (N = 8,000).Results\n\nIn the primary analytic sample (n = 61), 24 studies (39%) stated no missing-data strategy. MNAR sensitivity analysis was reported in 18/61 (30%), but formal MNAR models were used in only 5/61 (8%). Critically, tipping-point analysis was not reported in any of the 61 included studies (0%); delta adjustment was reported in only 1/61 (2%). Never-responders were distinguished from dropout in only 17/61 (28%). In the simulated cohort, non-response was substantial and selective. In the primary scenario, the complete-case estimate was 1.21 PROM points (95% CI: 0.69-1.72), the single-stage IPW estimate was 1.22 (95% CI: 0.69-1.74), and the two-stage IPW estimate was 1.23 (95% CI: 0.67-1.79). The tipping point was δ* = 7.2 points (classified as sensitive under the illustrative thresholds). In the secondary scenario, the corresponding tipping point was δ* = 30.5 points (classified as stable).Conclusions\n\nTipping-point analysis was not reported in the accessible methods or supplementary material of any of the 61 included registry-PROM studies; delta adjustment was reported in only one study as a sensitivity check. The five formal MNAR analyses identified used methods that typically require specialist expertise and stronger modeling assumptions than are routinely feasible in many registry settings. A pragmatic workflow using IPW and tipping-point analysis can close this gap without specialist infrastructure.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Diagnostics and Intervention/Surgery, Umeå University, Umeå, Sweden. johan.e.eriksson@umu.se."
  ],
  "article_type": "scoping review",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Johan Eriksson",
      "orcid": "0000-0002-4653-2067"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e6gyqstxw8bad.bib",
  "cited_by": 0,
  "doi": "10.1186/s12874-026-03058-x",
  "fulltext_url": "https://europepmc.org/articles/PMC13682039",
  "id": "e6gyqstxw8bad",
  "issns": [
    "1471-2288"
  ],
  "landing_url": "https://europepmc.org/article/MED/42897463",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R5-920"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7222917481",
  "pdf_url": "https://europepmc.org/articles/PMC13682039?pdf=render",
  "pmcid": "PMC13682039",
  "pmid": "42897463",
  "published": "2026-10-19",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/e6gyqstxw8bad.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42897463",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42897463"
    },
    {
      "first_seen": "2026-10-30T13:46:03Z",
      "native_id": "860da3b0193a45a589e9c1bde49b0e33",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s12874-026-03058-x"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Statistical Methods and Bayesian Inference",
      "subfield": "statistics"
    }
  ],
  "title": "A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example.",
  "title_text": "A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:03Z",
  "url": "https://research.draconitic.com/paper/e6gyqstxw8bad",
  "venue": "BMC medical research methodology",
  "versions": []
}