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models; predicting density and uncertainty surfaces from those models; evaluating predictions with contemporaneous data not included in the models as well as knowledge from the literature; and
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on identifying and validating surrogate endpoints for overall survival using data from cancer clinical trials and patient registries, developing prognostic models of clinical outcomes in cancer, and conducting
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, predictive modeling, machine learning, and causal inference methods. Experience with claims-based or EHR-based phenotyping, variable construction, treatment pattern analyses, healthcare utilization studies
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R or Python programming Tissue culture techniques Animal models Interest in translational cancer research, immunotherapy, and neuro-oncology. Other Requirements Ability to work with patient-derived