A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example.
- 1Department of Diagnostics and Intervention/Surgery, Umeå University, Umeå, Sweden. [email protected].
Abstract
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.
Related
- A computational comparison of handling distance constraints in MINLP
- INI-VPINN: A Variational Physics-Informed Neural Network with Implicit Neumann and Interface Handling for Multi-Material Domains with Geometric Singularities
- Constraint Learning by Optimizing over Trained Neural Networks: A Pragmatic Tutorial
- Handling Covariate Mismatch in Federated Linear Prediction
- Minimax-Optimal Robust Identification of Continuous-Time Systems: Handling Narrow-Band Disturbances in the Frequency Domain
BibTeX
@article{eriksson2026pragmatic,
title = {{A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example.}},
author = {Johan Eriksson},
journal = {BMC medical research methodology},
year = {2026},
month = oct,
doi = {10.1186/s12874-026-03058-x},
url = {https://europepmc.org/article/MED/42897463},
note = {CC BY},
}
RIS
TY - JOUR
TI - A pragmatic workflow for handling missing PROM data in registry studies: a methodological scoping review and simulated worked example.
AU - Johan Eriksson
PY - 2026
DA - 2026/10/19
JO - BMC medical research methodology
DO - 10.1186/s12874-026-03058-x
UR - https://europepmc.org/article/MED/42897463
AB - 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.
SN - 1471-2288
ER -