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In Oncology in particular, PoC decisions are often further challenged by the lack of control group in early trials, resulting in low rates of \u201ccorrect\u201d PoC decisions. The advent of large observational RealWorld Evidence (RWE) databases presents an opportunity to sharpen and improve PoC decisions in Oncology. These databases provide valuable information about patient outcomes when treated with standardofcare or competitor therapies, and may prove useful for earlyphase PoC clinical trials in several ways, such as (1) trialdesign considerations, in particular selection of the target study population; (2) prespecification of the criteria for declaring PoC; and (3) implementing PoC decisions once the study has been completed. Causal inference techniques are likely to be useful for all three objectives, as a means to synthesize RWE data and emerging singlearm clinicaltrial data, and attempt to draw causal comparisons between the patient outcomes on the new drug and standardofcare therapies. In such an analysis, one must take diligent care to account for potential confounding factors (aside from the difference in treatment received) that may lead the internal trial population to experience different outcomes from patients in the observational database. Examples of potential methods for inference on causal estimands based on nonrandomized data include outcome modelling, inverseprobability weighting, propensity score matching, doubly robust estimation, or Bayesian methods such as Bayesian causal forests. As part of this internship project, you will explore these methods using a nonsmallcell lungcancer (NSCLC) RWE database, and evaluate their performance, through data analysis and simulations, at guiding PoC decisions. Responsibilities: Develop a familiarity with causalinference methods for constructing external control arms using RWE, and assess their feasibility in the PoC setting Gain access to the Flatiron NSCLC ClinicoGenomic database through Novartis's stateoftheart data42 analytics platform Develop, through simulations and retrospective analysis of past PoC decisions, recommendations the usage of RWE to inform PoC trial design, determine criteria for PoC, and inform PoC decisions Communicate your findings to key stakeholders across Novarits Contribute to the positive impact that increasing availability and utility of RWE will have on the development of new therapies Knowledge and experience in at least one programming language for data analysis (e.g. R, Python) Knowledge of causal inference and/or experience with observational data are a plus Highly collaborative, ability to work across various Novartis groups such as Advanced Exploratory Analytics, Early Development Analytics Oncology, and the NIBR Translational Data Sciences Committee Requirements: Candidate should be in a US accredited degreegranting college\u200b or university pursuing an graduate degree in a quantitative field (e.g. Statistics / Biostatistics / Epidemiology / Data Science / Machine Learning) Why Novartis? 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