ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2025
Reducing patient burden of PROMs in healthcare through advanced computerized adaptive testing stopping rules.
Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating patient-reported outcomes in randomized controlled trials of targeted therapy and/or immunotherapy for liver cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Development and content validation of a health assessment item bank for regional health big data: a sequential mixed-methods approach.Health and quality of life outcomes · 2026Article
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Authors and funding
5 authors.
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Abstract
purposeApplication of computerized adaptive testing (CAT) can improve the assessment of patient-reported health outcomes by reducing patient burden. We aimed to reduce patient burden of CATs further by optimizing a standard error reduction stopping rule (SER; minimum change in SE(θ) after each CAT step).
methodsWe extracted PROMIS Anxiety and Depressive Symptoms CAT responses (mean age = 13.7, male = 50.3%) from the Dutch-Flemish PROMIS Assessment Center and estimated theta levels (θ) and standard errors (SE(θ)) for each step. The default stopping rules were a minimum/maximum of 4/12 items administered, respectively, or a minimum precision of SE(θ) < 0.32. We imposed increasing SER thresholds (0.01-0.20) and compared the following outcome criteria: mean efficiency of the CAT (M
resultsDefault stopping rules showed a mean efficiency of 0.88 and1.27, Mn
conclusionsWe conclude that the current approach allows us to determine an optimal SER threshold that improves measurement efficiency, especially when floor/ceiling effects are present in the target population. The threshold values will vary depending on the θ distribution of the target population and the IRT model parameters.
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