Oncology

Feasibility assessment limits many oncology RCT emulations using commercial EHR datasets

The CARE Initiative screened 54 RCT–RWD combinations across six US EHR sources and advanced only nine to detailed feasibility assessment. Only three combinations proceeded to emulation protocol development because of gaps in eligibility, biomarkers, performance status, and outcome capture. The study concludes that systematic, multi-stage fitness assessments are required before emulating oncology RCTs with EHR-based RWD. Authors suggest improving data fitness via linkage to claims or oncology-specific sources or through targeted chart abstraction. 1

Markov model estimates 1L olaparib increases aggregate survival and offers modest additional ROV

A Markov model estimated aggregate survival of 3.16 years with first-line olaparib versus 1.81 years with physician's choice chemotherapy without future innovations. Including anticipated future innovations raised modeled survival further to 3.91 versus 2.42 years in the high-ROV scenario. Relative survival gains across ROV scenarios ranged from 1.6% to 10.3%, with absolute incremental gains modest when adding innovation. Findings are modeling-based and dependent on assumptions about future therapies and uptake. 2

4R team model improved timing and sequencing across breast and lung cancer care

Implementation of the 4R Oncology model improved timing and sequencing metrics across 11 interdependent care types in community practice. Significant gains included lung surgery timing (88% v 72%) and breast gene-expression result timing (70% v 34%). The composite Optimization Index rose substantially in intervention cohorts (mean 0.82 v 0.68), with 4R contributing more than patient characteristics. This provides a practical roadmap for institutions aiming to operationalize high-functioning oncology teams. 3