ArticlePloS one2024
Propensity score matching as an effective strategy for biomarker cohort design and omics data analysis.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed.
- Article
- Surgical outcomes and follow-up analysis of valvular heart disease combined with left ventricular excessive trabeculation: a retrospective propensity score matching study.Journal of cardiothoracic surgery · 2026Article
- C1orf50 Drives Malignant Melanoma Progression Through the Regulation of Stemness.Cancer genomics & proteomics · 2025Article
- Article
- The 2024 Report on the Human Proteome from the HUPO Human Proteome Project.Journal of proteome research · 2024Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundAnalysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors.
methodsWe explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results.
resultsUnlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts.
conclusionPSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery.
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