A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example

Background Patient-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 A 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 In 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 Tipping-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.