ArticleCPT: pharmacometrics & systems pharmacology2023
Can a propensity score matching method be applied to assessing efficacy from single-arm proof-of-concept trials in oncology?
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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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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.
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Who cites it
6 citing papers in PubMed.
- Adjuvant Tegafur-Uracil Improves Survival in Low-Risk, Mismatch Repair Proficient Stage IIA Colon Cancer: A Propensity Score-Matched Analysis.Life (Basel, Switzerland) · 2025Article
- Clinical modeling of motor function to predict treatment efficacy and enable in silico treatment comparisons in infantile-onset Pompe disease.CPT: pharmacometrics & systems pharmacology · 2025Article
- Resident Physician Recognition of Tachypnea in Clinical Simulation Videos in Japan: Cross-Sectional Study.JMIR medical education · 2025Article
- Tumor growth inhibition-overall survival modeling in non-small cell lung cancer: A case study from GEMSTONE-302.CPT: pharmacometrics & systems pharmacology · 2024Article
- The efficacy of orally administered L-carnitine in alleviating ovarian dysfunctions has laid the foundation for targetedFrontiers in endocrinology · 2024Article
- Can a propensity score matching method be applied to assessing efficacy from single-arm proof-of-concept trials in oncology?CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a result of the escalating number of new cancer treatments being developed and competition among pharmaceutical companies, decisions regarding how to proceed with phase III trials are frequently based on findings from either single-arm phase I expansion cohorts or phase II studies that compare the efficacy of the study drug to a standard-of-care benchmark derived from historical data. However, even when eligibility criteria are matched, differences in the distribution of baseline patient features may influence the outcome of single-arm trials in real-world scenarios. Therefore, novel methods are needed to enhance the accuracy of efficacy prediction from current cohorts relative to historical data. In this study, we demonstrated the feasibility of using the propensity score matching (PSM) method to improve decision making by matching relevant baseline features between current and historical cohorts. According to our findings, utilizing the PSM method may provide a less biased means of comparing outcomes between current and historical cohorts relative to a naïve approach, which relies solely on differences in average outcomes between the cohorts.
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