I am a clinical epidemiologist, biostatistician, and pharmacist with a primary research interest in the use of biological, chemical, and clinical markers to guide decisions regarding the most appropriate use of medicines (precision medicine). My research focuses on the use of machine learning and advanced biostatistical methods to predict treatment efficacy and toxicity - particularly in the area of anti-cancer medicines. This research involves analysis of big data - in particular, pooling of data across multiple clinical trials. Additionally, I research novel biomarkers to predict drug exposure which will enable personalisation of medicine dosing.
I have deep expertise in many areas of clinical data science, including prediction models (machine learning), survival analysis, evaluation of treatment heterogeneity, clinical trial analysis, and meta-analysis. I have a high degree of proficiency in both R and python programming languages.
Selected Research Grants
From Big Data to Precision Medicine: Integrated analysis of clinical trial and electronic health record data in lung cancer. Sorich MJ. Cancer Council SA / Beat Cancer (2019-2023).
ADMExosomes: A new paradigm for tracking variability in drug exposure. Rowland A, Sorich MJ, Makenzie P. NHMRC Project Grant (2019-2021)
Improving the evaluation of new cancer therapies to expedite patient access. Karnon J, Sorich MJ, Ward R, Latimer N, Coory M. Project grant. NHMRC Project Grant (2017-2019)
Member, NHMRC Grant Review Panel Working Committee
Member, NHMRC Grant Review Panel Working Committees (Ideas and Investigator Grant Schemes)
Member, Editorial Board, Therapeutic Advances in Drug Safety
Member, Editorial Board, Translational Cancer Research
Member, Editorial Board, Clinical Pharmacology & Translational Medicine
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