Target trial emulation to assess real-world efficacy in the Epidemiological Strategy and Medical Economics metastatic breast cancer cohort

BACKGROUND: Real-world data studies usually consider biases related to measured confounders. We emulate a target trial implementing study design principles of randomized trials to observational studies; controlling biases related to selection, especially immortal time; and measured confounders. METHODS: This comprehensive analysis emulating a randomized clinical trial compared overall survival in patients with HER2-negative metastatic breast cancer (MBC), receiving as first-line treatment, either paclitaxel alone or combined to bevacizumab. We used data from 5538 patients extracted from the Epidemiological Strategy and Medical Economics–MBC cohort to emulate a target trial using advanced statistical adjustment techniques including stabilized inverse-probability weighting and G-computation, dealing with missing data with multiple imputation, and performing a quantitative bias analysis for residual bias due to unmeasured confounders. RESULTS: Emulation led to 3211 eligible patients, and overall survival estimates achieved with advanced statistical methods favored the combination therapy. Real-world effect sizes were close to that assessed in the existing E2100 randomized clinical trial (hazard ratio = 0.88, P = .16), but the increased sample size allowed to achieve a higher level of precision in real-world estimates (ie, reduced confidence intervals). Quantitative bias analysis confirmed the robustness of the results with respect to potential unmeasured confounding. CONCLUSION: Target trial emulation with advanced statistical adjustment techniques is a promising approach to investigate long-term impact of innovative therapies in the French Epidemiological Strategy and Medical Economics–MBC cohort while minimizing biases and provides opportunities for comparative efficacy through the synthetic control arms provided. DATABASE REGISTRATION: clinicaltrials.gov Identifier NCT03275311.