ArticleBMC medical research methodology2025
Handling missing values in patient-reported outcome data in the presence of intercurrent events.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Tutorial on Analyzing Patient-Reported Outcome Measures in Clinical Development.Clinical and translational science · 2026Review
- Surrogate Endpoints in CLL: From Promise and Pitfalls to a Context-Specific Validation Framework.Hematology reports · 2026Review
- How to Analyze Longitudinal Patient-Reported Outcomes in Populations With High Mortality Rates.Journal of the American Geriatrics Society · 2026Article
- Are we reporting well enough? A systematic survey of missing data in patient-reported outcomes from non-small cell lung cancer randomized trials.Health and quality of life outcomes · 2026Article
- Letter to the editor: Reply to Chen et al.Clinical and translational radiation oncology · 2025Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionAs patient-reported outcomes (PROs) are increasingly used in the evaluation of medical treatments, it is important that PROs are carefully analyzed and interpreted. This may be challenging due to substantial missing values. The missingness in PROs is often closely related to patients' disease status. In that case, using observed information about intercurrent events (ICEs) such as disease progression and death will improve the handling of missing PRO data. Therefore, the aim of this study was to develop imputation models for repeated PRO measurements that leverage information about ICEs.
methodsWe assumed a setting in which missing PRO measurements are missing at random given observed measurements, as well as the occurrence and timing of ICEs, and potentially other (baseline or time-varying) covariates. We then showed how these missingness assumptions can be translated into concrete imputation models that also account for a longitudinal data structure. The resulting models were applied to impute anonymized PRO data from a single-arm clinical trial in patients with advanced lung cancer.
resultsIn our trial example, accounting for death and other ICEs in the imputation of missing data led to lower estimated mean health-related quality of life (while alive) compared to an available case analysis and a naive linear mixed model imputation.
conclusionInformation about the timing and occurrence of ICEs contribute to a more plausible handling of missing PRO data. To account for ICE information when handling missing PROs, the missing data model should be separated from the analysis model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.