Determining the Survival Impact and Cost‐Effectiveness of Multi‐Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches

Objective To determine the effectiveness and cost‐effectiveness of multi‐gene panel sequencing compared to single‐gene KRAS testing for metastatic colorectal cancer (mCRC). Study Setting and Design British Columbia, Canada (BC) is a provincial single‐payer public healthcare system, and it was the first province to publicly reimburse multi‐gene sequencing for mCRC. Panels expand treatment de‐escalation by expanding RAS testing for more precise targeting of anti‐ EGFR therapies. Reimbursement of panels remains unequal across healthcare systems given uncertain clinical and economic impacts. Our quasi‐experimental study design followed the target trial emulation approach, emulating random treatment assignment with two different methods to examine the sensitivity of estimates: inverse probability of treatment weighting estimated with super learning (SL‐IPTW) and 1:1 genetic algorithm‐based matching, a machine learning approach. We then estimated mean three‐year survival time and costs (public healthcare payer perspective; 2021CAD) and calculated the incremental net monetary benefit (INMB) for life‐years gained (LYG) at $50,000/LYG using weighted linear regression and nonparametric bootstrapping, also accounting for inverse probability of censoring weights. Our sensitivity analysis estimated LYG using targeted minimum‐based loss estimation (TMLE), a doubly robust approach that also uses super learning. Data Sources and Analytical Sample Patient‐level linked administrative health databases capturing cancer and non‐cancer care for all BC adults with a metastatic colorectal cancer between 2016 and 2019. Principal Findings Our study included 892 patients (84.3%) receiving multi‐gene panels and 166 (15.7%) receiving single‐gene testing. INMB estimates were similar for SL‐IPTW ($20,397; 95% CI: $9317, $34,862) and matching ($19,569; 95% CI: $8509, $31,447), with 99.3% and 98.8% probabilities, respectively, of panels being cost‐effective. We found statistically significant survival benefits with LYG of 0.31 (SL‐IPTW; 95% CI: 0.04, 0.54), 0.25 (matching; 95% CI: 0.03, 0.47) and 0.19 (TMLE; 95% CI: 0.02, 0.37). Conclusions Survival impacts were robust to super learning approaches. Real‐world evidence demonstrates that reimbursing multi‐gene sequencing for more precise targeting of mCRC treatments provides value for healthcare systems and clinically important benefits to patients.