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  • Writer's pictureRyon

Recent Publication: Causal Inference, Modeling Physician Intuition and Clinical Utility

Hello Friends,

I'm proud to report a new publication. In lieu of a long discussion about the hurdles (and immense potential) of integrating real-world data to drive medical evidentiary advances, statistical techniques and causal frameworks deserving to be more widely adopted, and how our recent publication tries to sing these tunes, I will instead try to give a TLDR version:

  • Diagnostics exist to improve decision-making

  • We sought to better understand the decisions made without the diagnostic, to better infer causality of outcomes with the diagnostic

  • We utilized real world data from of standard of care treatment

  • We “borrowed” causal inference techniques from epidemiology and economics (i.e. propensity analyses)

  • We trained a risk score using existing clinical and laboratory features to predict differential drug class survival, and made this model compete with the AR-V7 biomarker. AR-V7 still out-performed the "poor man's version" of the biomarker.

Graf RP, Hullings M, Barnett ES, Carbone E, Dittamore R, Scher HI. Clinical Utility of the Nuclear-localized AR-V7 Biomarker in Circulating Tumor Cells in Improving Physician Treatment Choice in Castration-resistant Prostate Cancer. European Urology. 2020. PubMed

I will say that this was definitely one of the more stimulating and challenging projects I’ve had in recent years (not to mention the 6 peer-reviewers and staff statistical reviewer. European Urology doesn’t mess around!). It was a great experience and an honor to work closely with the team at MSKCC, and I have to thank Ryan Dittamore and Howard Scher for supporting and seeing the value in this project. We hope that these methods of causal inference and counterfactual testing of real-world evidence will become more common in the years to come, becoming an integral part of biomarker evidentiary builds.



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