ASAlessandria Struebing

Alessandria Struebing

*This is an algorithmically generated author record based on publicly available information.

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Alessandria Struebing has a total of 22 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer

    by Alessandria Struebing, Chelsea McKibbon, Haoyao Ruan, Emma Mackay, Natalie Dennis, Russanthy Velummailum, Philip He, Yoko Tanaka, Yan Xiong, Aaron Springford, et al.

    Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small-cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using ConcertAI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21–1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73–1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08–1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86–1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.


  • Open Access

    Real-world treatment patterns, healthcare resource utilization and costs among patients with peripheral T-cell lymphoma

    by Charles Dharmani, Sudhir Unni, Ngan Pham, Nazneen Fatima Shaikh, Yan Xiong, Rohan Vashi, Oluwatosin Fofah, Alessandria Strubing, Maribel Salas, Nora Tu, et al.

    Objective: To evaluate treatment patterns, healthcare resource utilization (HRU) and costs among peripheral T-cell lymphoma (PTCL) patients in the USA. Methods: A retrospective cohort study, using ... Plain language summary – How are patients with peripheral T-cell lymphoma treated in the real world & how much does it cost? Peripheral T-cell lymphomas (PTCL) are a rare and fast-growing form of b... Tweetable abstract Treatment patterns and costs in peripheral T-cell lymphoma.