Courses Offered in 2016-2017
Biostatistics Course / Epidemiology Courses / Health Systems, Policy & Management Courses / Infection disease Courses
Environmental & Occupational Health Courses / Population & Global Health Courses / General Public Health Courses /
This course introduces basic statistical concepts and methods. The emphasis of the course is on practical applications: choosing the correct method for particular datasets and correct interpretation of the analysis results. Examples from different disciplines of public health including chronic and infectious disease epidemiology, environmental health, and health policy will be used to illustrate the use of biostatistical methods in answering important public health questions.
This course will provide a foundation for the practical analysis of data for which the primary outcome is a continuous variable. The course will begin with an introduction to “real-world” data analysis with a motivating example looking at predictors of infant birth weight in Hong Kong. Methods for multivariate analysis of predictors of continuous outcomes including one-way and two-way ANOVA and multiple linear regression will then be discussed in detail with an emphasis on correct use of these methods in practice.
This course will provide a foundation for the practical analyses of categorical and time to event (survival) data. The course will cover the use of logistic regression models for use with binary outcomes and Cox proportional hazards regression models for time to event outcomes. Practical application of these models will be emphasized and model building and the checking of model assumptions will be covered in detail.
The objective of this course is to provide background of regulation of drugs, devices and biological development. We will apply the principles of ICH-Good Clinical Practice in clinical research and discuss the role and responsibilities of key parties described in the document. We will describe the requirements of essential documentation and adverse event reporting. Scenarios will be given to the students to strengthen their understanding of practical application of ICH-GCP to the clinical trial process. (Pre-requisite or recommended background: 1. Familiar with Declaration of Helsinki; 2. Clinical Research Personnel)
The objective of this course is to provide students with a theoretical and practical knowledge of the issues involved in the design, conduct, analysis and interpretation of randomized clinical trials. Attention will be given to the problems of conducting clinical trials in both single centre and multi-centre, and covers trials initiated by industry as well as trials in academic setting. Students will be trained to develop skills to properly design clinical trial, critically analyze and carry out research and to communicate effectively.
The objective of this course is to familiarize students with the SAS software for pharmaceutical application. The course starts with the introduction of basic SAS skills followed by using SAS to draw tables, figures, and listings (TFL) and to analyze medical data. Practical scenarios will be given to students to understand the needs of SAS in pharmaceutical industry
This course will cover advanced statistical modeling techniques for use with complex datasets. Topics will include Poisson and Negative Binomial regression for count outcomes, repeated measures ANOVA, GEE models and multilevel models for longitudinal data and multilevel models for clustered data. Upon completion of this course students will understand the reasons that more complex statistical models need to be used for datasets for which the assumptions of linear or logistic regression are not valid, such as datasets with ordinal or count outcomes, longitudinal or clustered data, and data with non-linear associations between variables. They will understand which models should be used for each of these situations, how to fit and interpret these models, and how to check the assumptions of these models.
This course will cover methods importance in the analysis of data collected from questionnaires. Both exploratory and confirmatory factor analysis (under the framework of Structural Equation Models) will be discussed.
The course will provide a broad overview and introduction to bioinformatics and its applications in pharmaceutical industry. Topics will cover (1) basic bioinformatics methods: hierarchical clustering, lasso, random forest, LDA, PCA, boosting, bootstrapping, etc. (2) data sequencing and management: microarray data, GWAS data, the raw data treatment and analysis method, batch effect and normalization, parallel programming in R; (3) phylogenic analysis; (4) Chemo-bioinformatics modeling, 3D structure, chemical - protein relation leading to drug discovery.