My name is Darren Qiang, I completed my Bachelors in Biomedicine (Immunology) 2021 and Masters in public Health (Epidemiology / Statistics) 2023 at the University of Melbourne. Concurrently with my studies, I have also worked as a research officer in epidemiological data analysis and policy at Dental Health Services Victoria.

During my post graduate studies, developed a passion for research, particularly in statistics and epidemiology. This passion for research was really solidified when I was able to understand and see the real life impacts such research can have on patients and populations. Throughout this period, I have gained experience in conducting quantitative data analysis and experimental design. However, I still felt a strong inclination to both enhance and broaden my research skills in statistical methods and epidemiology, acknowledging that a PhD would be the next step.

My new research is able to draw upon both my previous understanding of neuro-immunology and my skill set in statistics and epidemiology.

Multiple sclerosis (MS) is the second most common cause of disability in young adults, and is associated with significant societal costs. It is a  challenging autoimmune condition characterized by episodic inflammation that may lead to secondary neurodegeneration in the central nervous system (CNS). Timely initiation of the right disease-modifying therapy (DMT) is the cornerstone of successful therapeutic management as it plays a critical role in preventing further worsening of disability. Despite recent advances in MS pharmacotherapy, disability prevention in patients has remained challenging and often suboptimal. Recent research has highlighted the potential of predictive algorithms that integrate clinical and biological data to guide treatment decisions in MS.

This doctoral research endeavours to propel the field forward by developing an enhanced predictive algorithm – “Crystal Ball 2.0”.  This innovative algorithm builds upon a previously established predictive algorithm, which demonstrated that individual response to the commonly used DMTs can be predicted, albeit to a limited extent, based on patients’ clinical and demographic characteristics. During this PhD, I will further develop this predictive algorithm by incorporating novel biomarkers, aiming to augment the precision of predicting individual responses to DMTs. Furthermore, I will investigate a rapidly developing intersection in epidemiology and statistics, by exploring causal effects of therapies in individuals as opposed to mere associations. The study is a collaborative, multi-site effort spanning Australia, Sweden, Switzerland, and the Czech Republic.

Supervisors: Prof Tomas Kalincik, A/Prof Charles Malpas, Dr Sifat Sharmin, Dr Izanne Roos