ArticlePain medicine (Malden, Mass.)2024
Utility of the PICOTS framework to assess clinical trial disruptions: monitoring the impact of COVID-19 in the Pain Management Collaboratory.
Article in Pain medicine (Malden, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
objectiveDespite careful design of clinical trials, unforeseen disruptions can arise. The PICOTS (Patient population, Intervention, Comparator, Outcomes, Timepoints, Setting) framework was used to assess disruptions in pain management research imposed by coronavirus disease 2019 (COVID-19) within the Pain Management Collaboratory.
methodsRapid qualitative methods were employed to identify trial disruptions due to COVID-19 in 11 pragmatic clinical trials of nonpharmacological approaches for pain management. The PICOTS framework was applied by investigators of 4 Collaboratory trials selected to cover 4 types of trial designs (individually randomized, stepped-wedge, cluster, sequential multiple assignment randomized trial-SMART). Interviews with the lead investigators of these trials were completed, and findings were presented/discussed on video calls over a 6-month period (March-August 2021) from which themes/lessons learned were identified and consensus reached.
resultsInvestigators indicated that patient populations remained generally stable. A major COVID-19 trial disruption was moving from in-person to virtual care affecting delivery of interventions/comparators and outcome assessments. The resultant mixed-mode of care delivery created issues with intervention fidelity posing analytic challenges. COVID-19 also induced ongoing/intermittent delays and other barriers to accessing primary and specialty care at some facilities, creating research capacity issues affecting delivery of experimental interventions requiring sustained, reliable participation of clinical partners. Study designs most affected by COVID-19 were stepped-wedge (intervention/comparator changing over time), cluster (increased site variability inflating intracluster correlation), and SMART (second-stage randomizations disrupted); stratified individually-randomized trials were less vulnerable because of individual-level randomization.
conclusionsPICOTS provides a framework for assessing the impact of trial disruptions in a structured manner. Given the COVID-19 experience, it is important for researchers to consider the potential impact of future trial disruptions during study planning.
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Registered trials
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