BIOSTATISTICS AND HEALTH DATA SCIENCE
Center for Quantitative Medicine (CQM) is an academic group consisting of statistical methodologists, applied biostatisticians and epidemiologists, who strive to advance medicine and health by quantitative methods. Modern biomedical research is complex in that there are huge arrays of laboratory and measurement methods, data sources, and study designs. It is further complicated by the need for interdisciplinary collaboration across clinical, translational and quantitative sciences. CQM aims to ensure that studies are appropriately designed, analyzed and reported, by developing state-of-the-art biostatistical methods that are directly relevant to the advancement of medicine, bringing together the quantitative and biomedical science communities together, and educating quantitative and biomedical scientists. Our focus is on clinical and epidemiological research in an increasingly complex biomedical research environment.
Main Research Areas:
• Adaptive clinical trial designs and dynamic treatment regimes
• Analysis of high dimensional data, including microarray, genome-wide association studies, and next generation sequencing (NGS).
• Analysis of time-to-event, recurrent event, and censored biomedical data
• Diagnostic medicine and measurement methods
• Longitudinal data analysis
1. At least 30 modular credits, including a core module for all Duke-NUS PhD students “Molecules to Medicines” and two core modules for PhD Biostatistics & Bioinformatics students “Core Concepts in Biostatistics” and “Core Concepts in Bioinformatics”. Courses will be chosen with input from the PhD supervisor and committee.
2. Participation in graduate student seminars and journal club.
3. Conduct of original research and successful completion and defense of the research thesis.
Bioinformatics is an integration of data analytics, statistics, machine learning, modelling, software engineering, and computer science to answer questions in basic and translational biomedical research. The recent explosion of demand for bioinformatics in the last five years has been driven partly by huge decreases in the cost of next generation DNA sequencing, which is 10,000 times cheaper than it was in 20061. As a result, next-generation sequencing is now a foundational technology for much of biological research. Bioinformatics for genomics and basic and translational biology and bioinformatics approaches to analysing next-generation DNA and RNA sequencing data is now essential to biomedical research. The rapid development of many other high throughput technologies is also driving demand for bioinformatics experts.
Main Research Areas:
The programme comprises all areas of current bioinformatics practice and research, including genome informatics, bioinformatics for next-generation sequencing, modeling of biological processes, and image analysis for biology, including neuroimaging.
1. Completion of a total of 60 modular credits (MCs).
2. 36 MCs will come from the student’s dissertation research at the mentor’s laboratory, and remaining 24 MCs from coursework (including two laboratory rotations and research seminars).
3. Participation in graduate student seminars and journal club.
4. Attendance at software training workshops (R, SAS, and/or Stata).
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