2020
DOI: 10.1093/jamiaopen/ooaa048
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Framework for identifying drug repurposing candidates from observational healthcare data

Abstract: Objective Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs. Material… Show more

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Cited by 22 publications
(36 citation statements)
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“…Finally, we used Benjamini and Hochberg's (1995) method to correct for multiple hypothesis testing and considered adjusted p-values ≤ 0.05 as statistically significant. For a full description of the RWD-based drug repurposing framework see our methodological paper (Ozery-Flato et al, 2020). Ground truth effects (that is, RCT-validated) are typically unavailable for drug repurposing candidates; notably, however, the estimated effects showed significant correlation across different algorithms and data sources (adjusted p-value < 0.05 for all comparisons across outcomes, databases, and causal inference algorithms), attesting to the robustness of the framework.…”
Section: Discussionmentioning
confidence: 95%
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“…Finally, we used Benjamini and Hochberg's (1995) method to correct for multiple hypothesis testing and considered adjusted p-values ≤ 0.05 as statistically significant. For a full description of the RWD-based drug repurposing framework see our methodological paper (Ozery-Flato et al, 2020). Ground truth effects (that is, RCT-validated) are typically unavailable for drug repurposing candidates; notably, however, the estimated effects showed significant correlation across different algorithms and data sources (adjusted p-value < 0.05 for all comparisons across outcomes, databases, and causal inference algorithms), attesting to the robustness of the framework.…”
Section: Discussionmentioning
confidence: 95%
“…In a preliminary method development study (Ozery-Flato et al, 2020), we validated the drug repurposing framework used here. We had demonstrated that treatment effects estimated across different data sources and causal methodologies showed a high degree of agreement (p-value < 0.05 for all comparisons).…”
Section: Discussionmentioning
confidence: 99%
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“…As seen in the Methods section, the previous works using RWD in repurposing illustrates various repurposing strategies with different modalities of database used, which might be taken into account as a guide in designing a repurposing study at a given scope of data. It is noticeable that, when single modal RWD was used, another RWD (of the same modality) was also used for the validation purpose [ 14 18 19 ]. While most of the works tried to validate their repurposing results with another modality of data (e.g., results obtained from EMR were validated using genomic or multi-omic data or vice versa), it is hardly found that validation was made in human or in clinical trials.…”
Section: Discussionmentioning
confidence: 99%
“…Ozery-Flato et al [ 19 ] and Laifenfeld et al [ 20 ] presented a framework that systematically analyzes real-world longitudinal data for a large cohort of patients. Using causal inference methodology, the framework emulates a maximal number of RCTs based on observed healthcare data, while adjusting for selection and confounding biases.…”
Section: Methodsmentioning
confidence: 99%