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The development of high-throughput sequencing and targeted therapies has ledto the emergence of personalized medicine: a patient's molecular profile or thepresence of a specific biomarker of drug response will correspond to atreatment recommendation made either by a physician or by a treatmentassignment algorithm. The growing number of such algorithms raises the questionof how to quantify their clinical impact knowing that a personalized medicinestrategy will inherently include different versions of treatment.  We thus specify an appropriate causal framework with multiple versions oftreatment to define the causal effects of interest for precision medicinestrategies and estimate them emulating clinical trials with observational data.Therefore, we determine whether the treatment assignment algorithm is moreefficient than different control arms: gold standard treatment, observedtreatments or random assignment of targeted treatments.  Causal estimates of the precision medicine effects are first evaluated onsimulated data and they demonstrate a lower biases and variances compared withnaive estimation of the difference in expected outcome between treatment arms.The various simulations scenarios also point out the different bias sourcesdepending on the clinical situation (heterogeneity of response, assignment ofobserved treatments etc.). A RShiny interactive application is also provided tofurther explore other user-defined scenarios. The method is then applied todata from patient-derived xenografts (PDX): each patient tumour is implanted inseveral immunodeficient cloned mice later treated with different drugs, thusproviding access to all corresponding drug sensitivities for all patients.Access to these unique pre-clinical data emulating counterfactual outcomesallows to validate the reliability of causal estimates obtained with theproposed method.

Causal inference with multiple versions of treatment and application to personalized medicine