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Silva Ljevar, MSc (she/her/hers)

Reserach Fellow
Unit of Biostatistics for Clinical Research Department of Epidemiology and Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Milan, Lombardia, Italy

Silva Ljevar is a fellow researcher with a Master's degree in Biostatistics from the University of Milan-Bicocca who has been contributing her expertise to the Unit of Biostatistics for Clinical Research at Fondazione IRCCS Istituto Nazionale dei Tumori since June 2021. At the Istituto Nazionale dei Tumori, Silva plays a crucial role in advancing oncological research. Her responsibilities encompass various aspects of statistical analysis and support throughout the research process, from study design, including sample size calculation and planning the statistical analysis, to the final stages of writing scientific papers. Silva's work primarily focuses on several key areas within biostatistics: survival analysis, predictive models and biomarkers construction, applied both in observational studies and clinical trials. Silva has primarily applied her expertise in the fields of hematology, sarcomas, and breast cancer, where her contributions have a direct impact on improving patient care and advancing our understanding of these diseases. In addition, she has authored or co-authored a total of eight scientific articles published in journals with impact factors, one of which as the first co-author. For the past two years she has also helped as a tutor for the courses “Excel Laboratory” and “Data Collection and Graphical Representation” at the University of Milan. Silva is currently enrolled in a Ph.D. programme at The Open University focusing on statistical methodologies for small clinical trials, research area particularly important for small study populations, such as patients with rare tumors. Silva is a multilingual professional, fluent in Serbo-Croatian as her mother tongue, and proficient in both English and Italian at an advanced level. Furthermore, Silva possesses advanced proficiency in statistical programming languages, particularly R and SAS.

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