REVERSE model: a novel approach in medical research
Abstract Background Randomized controlled trials are considered the gold standard but they are limited by high costs and external validity. The REVERSE model is introduced to address these challenges. Methods The REVERSE model encompasses two sequential phases. First, in the data mining phase, compa...
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BMC
2025-07-01
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| Series: | Trials |
| Online Access: | https://doi.org/10.1186/s13063-025-08974-9 |
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| author | Luca Saba Gianluca De Rubeis Francesco Pisu |
| author_facet | Luca Saba Gianluca De Rubeis Francesco Pisu |
| author_sort | Luca Saba |
| collection | DOAJ |
| description | Abstract Background Randomized controlled trials are considered the gold standard but they are limited by high costs and external validity. The REVERSE model is introduced to address these challenges. Methods The REVERSE model encompasses two sequential phases. First, in the data mining phase, compatible datasets are identified and merged by using matching or stricter inclusion/exclusion criteria, thereby reducing selection bias. Second, a randomization phase addresses the inherent biases of the selected datasets. For a dichotomous scenario, the data are organized into four sub-cohorts according to the concordance with the original and new assignments: two concordant and two non-concordant. New decision factors are tested in concordant groups. Patients in non-concordant cohorts were excluded. ROMICAT-II was used to reproduce the findings from both the ROMICAT-II and ROMICAT-I trials, with results reported as the median of 10,000 applications. Findings The REVERSE model successfully replicated the results of ROMICAT-II and ROMICAT-I using only ROMICAT-II data. For ROMICAT-II, the median (interquartile range) of all median differences between length of hospitalization stay with cardiac computed tomography angiography (CCTA) and standard diagnostic strategy after 10,000 applications matched the trial’s findings 100% of the time (18.06 h [17.76–18.32] vs. 18.1 h; p < 0.05). For ROMICAT-I, median of all REVERSE plaque prevalence (PP) at CCTA matched the observed PP at CCTA from ROMICAT-I (49.63% [48.2–51.2] vs. 49.7%). The REVERSE PP fell within 49.63% ± 5% in 9733 (97.33%) applications. Conclusion The REVERSE model allows repurposing existing datasets to explore novel research questions while mitigating inherent biases through stringent inclusion criteria matching and randomization. |
| format | Article |
| id | doaj-art-c086cf68c5eb49b2a812ebfb2d8fe167 |
| institution | Kabale University |
| issn | 1745-6215 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Trials |
| spelling | doaj-art-c086cf68c5eb49b2a812ebfb2d8fe1672025-08-20T04:03:07ZengBMCTrials1745-62152025-07-012611910.1186/s13063-025-08974-9REVERSE model: a novel approach in medical researchLuca Saba0Gianluca De Rubeis1Francesco Pisu2Department of Medical Imaging, Azienda Ospedaliero Universitaria (A.O.U.) of Cagliari-Polo Di Monserrato,, University of CagliariDepartment of Diagnostic UOC of Diagnostic and Interventional Neuroradiology, San Camillo-Forlanini HospitalDepartment of Medical Imaging, Azienda Ospedaliero Universitaria (A.O.U.) of Cagliari-Polo Di Monserrato,, University of CagliariAbstract Background Randomized controlled trials are considered the gold standard but they are limited by high costs and external validity. The REVERSE model is introduced to address these challenges. Methods The REVERSE model encompasses two sequential phases. First, in the data mining phase, compatible datasets are identified and merged by using matching or stricter inclusion/exclusion criteria, thereby reducing selection bias. Second, a randomization phase addresses the inherent biases of the selected datasets. For a dichotomous scenario, the data are organized into four sub-cohorts according to the concordance with the original and new assignments: two concordant and two non-concordant. New decision factors are tested in concordant groups. Patients in non-concordant cohorts were excluded. ROMICAT-II was used to reproduce the findings from both the ROMICAT-II and ROMICAT-I trials, with results reported as the median of 10,000 applications. Findings The REVERSE model successfully replicated the results of ROMICAT-II and ROMICAT-I using only ROMICAT-II data. For ROMICAT-II, the median (interquartile range) of all median differences between length of hospitalization stay with cardiac computed tomography angiography (CCTA) and standard diagnostic strategy after 10,000 applications matched the trial’s findings 100% of the time (18.06 h [17.76–18.32] vs. 18.1 h; p < 0.05). For ROMICAT-I, median of all REVERSE plaque prevalence (PP) at CCTA matched the observed PP at CCTA from ROMICAT-I (49.63% [48.2–51.2] vs. 49.7%). The REVERSE PP fell within 49.63% ± 5% in 9733 (97.33%) applications. Conclusion The REVERSE model allows repurposing existing datasets to explore novel research questions while mitigating inherent biases through stringent inclusion criteria matching and randomization.https://doi.org/10.1186/s13063-025-08974-9 |
| spellingShingle | Luca Saba Gianluca De Rubeis Francesco Pisu REVERSE model: a novel approach in medical research Trials |
| title | REVERSE model: a novel approach in medical research |
| title_full | REVERSE model: a novel approach in medical research |
| title_fullStr | REVERSE model: a novel approach in medical research |
| title_full_unstemmed | REVERSE model: a novel approach in medical research |
| title_short | REVERSE model: a novel approach in medical research |
| title_sort | reverse model a novel approach in medical research |
| url | https://doi.org/10.1186/s13063-025-08974-9 |
| work_keys_str_mv | AT lucasaba reversemodelanovelapproachinmedicalresearch AT gianlucaderubeis reversemodelanovelapproachinmedicalresearch AT francescopisu reversemodelanovelapproachinmedicalresearch |