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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 5,538 patients extracted from the Epidemio-Strategy-Medico-Economical (ESME) 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 (QBA) for residual bias due to unmeasured confounders. Results Emulation lead to 3,211 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 (HR 0.88, p = 0.16), but the increased sample size allowed to achieve a higher level of precision in real-world estimates (ie, reduced confidence intervals). QBA 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 ESME-MBC cohort while minimizing biases, and provides opportunities for comparative efficacy through the synthetic control arms provided. Database registration clinicaltrials.gov Identifier NCT03275311