Mission
Increasing importance is placed on evidence-based medicine, and the strength and quality of evidence used to guide clinical decisions. Clinical research studies require careful design and analysis in order to permit robust scientific inferences.
Recognising the need for core support in biostatistics and clinical research methodology, in January 2015 the Li Ka Shing Faculty of Medicine created the Biostatistics and Clinical Research Methodology Unit to provide support on quantitative research methods.
The mission of the Unit is to support research activities in the Faculty of Medicine through provision of expert advice on biostatistics and clinical research methodology. The objectives of the Unit are to provide expert advice on various aspects of biostatistics and clinical research methodology, including:
- study design
- sample size calculations
- questionnaire design
- planning and implementation of statistical analysis
- analysis and interpretation of quantitative data
for grant applications, manuscripts for journal publication, and reports.
About Us
The Biostatistics and Clinical Research Methodology Unit is part of the Li Ka Shing Faculty of Medicine, and is hosted by the School of Public Health. The mission of the Unit is to support research activities in the Faculty of Medicine through provision of expert advice on biostatistics and clinical research methodology. The Unit also provides statistical consultancy to public bodies and private companies.
How much do we charge?
Services are available to all staff and postgraduate students of the Li Ka Shing Faculty of Medicine. Technical support and consultancy services for grant applications are provided free of charge to HKUMed researchers; the Li Ka Shing Faculty of Medicine covers these fees. Other services are charged based on hourly or daily rates. Please contact us to discuss the potential costs of ongoing support for data analysis and interpretation; these costs vary from project to project. Faculty members are encouraged to budget for any anticipated costs of data management and analysis in their grant applications, and to discuss this with us in advance of their grant application.
Our Team
Permanent Staff
Dr Helen Zhi is Scientific Officer and Director of the Biostatistics and Clinical Research Methodology Unit. She holds a PhD and an MS in Statistics from Temple University, a BS in Probability and Statistics, and a BA in Economics from Peking University. Dr Zhi has clinical-trial experience from the pharmaceutical industry and served as lead statistician for submissions to the FDA (US), EMA (EU), Health Canada, and PMDA (Japan). Her experience includes cardiovascular, metabolic, oncology, virology, neurology, and musculoskeletal therapeutic areas.
Senior Consultants
Professor Ben Cowling is the Helen and Francis Zimmern Professor in Population Health, Chair Professor of Epidemiology, and Head of the Division of Epidemiology and Biostatistics in the School of Public Health at HKU. He earned a PhD in medical statistics from the University of Warwick (UK) in 2003 and spent a year as a postdoctoral researcher at Imperial College London (UK) before joining HKU in 2004. He has particular expertise in medical statistics, including generalized linear models, survival analysis, study design, and meta-analysis.
Consultants
Request Support
Basic Concepts
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Bar Chart
- Presents grouped data with rectangular bars with lengths proportional to the values that they represent
- Can be plotted vertically or horizontally
- Very useful for recording discrete data and showing comparisons
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Histogram
- Represents the distribution of numerical data
- Is used for continuous data, where the bins represent ranges of data
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Scatter Plot
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Displays values for two variables for a set of data
- Suggests various kinds of correlations between variables
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Ability to show nonlinear relationship between variables
High positive correlation Low positive correlation Negative correlation Uncorrelated Non-linear relationship
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Box Plot
- Shows descriptive statistics
- Outliers may be plotted as individual points
- Displays variation in samples of a statistical population without making any assumptions of the underlying statistical distribution.
- Spacing between the different parts of the box indicates the degree of dispersion (spread) and skewness in the data, and shows outliers.
How to understand a Boxplot
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Means and Error Plot
- Represents the mean and variability of data
- Represents the overall distribution of the data