Advanced Statistical Methods and Technology in Community Health Care
社區醫療高級統計方法與科技
This 12-week certificate course, taught by professional staff from the Department of Family Medicine and Primary Care (FMPC), the University of Hong Kong, with contributions from guest experts in statistics, data science, and real-world evidence research, equips healthcare professionals with advanced analytical skills for working with complex health data in community and primary care settings. Building on foundational research and statistical knowledge, topics progress from principles of statistical inference, linear and logistic regression modelling, and survival analysis to screening and prediction model evaluation, psychometric methods, machine learning, and the use of routinely collected health data for generating real-world evidence. The course places strong emphasis on practical implementation and interpretation through applied examples, coding exercises, and critical appraisal of published studies, enabling students to select appropriate analytical approaches, interpret findings, and translate quantitative results to inform clinical practice, health services planning, and policy decision-making.
Admissions
Applicants must hold a Bachelor of Medicine and Bachelor of Surgery (MBBS), or a Bachelor’s degree in Chinese Medicine, Dentistry, Nursing, Social Work, Occupational Therapy, Physiotherapy, Psychology, or another healthcare-related discipline from this University, or possess an equivalent qualification from another recognized university or institution.
Research assistants holding a degree in one of the above disciplines are also eligible to apply.
Undergraduate students are not eligible for admission.
Application Procedures
To register for the course, please fill in this form:
The application period will close on 4 December 2026.
Space is limited; registration is on a first-come, first-served basis.
Fees
HK$7500 (Full Course)
Payment Method
• By Cheque only
Please make your cheque payable to: "The University of Hong Kong"
Kindly write your full name and contact number on the back of the cheque before submission.
Please send the cheque to the following address:
Attn. Mr. Kenneth WONG
Department of Family Medicine and Primary Care
3/F, Ap Lei Chau Clinic
161 Ap Lei Chau Main Street
Ap Lei Chau, Hong Kong
Objectives
Background
Health research in community and primary care settings increasingly relies on advanced quantitative methods to generate robust evidence for clinical decision-making and health policy. While many healthcare professionals possess foundational research and statistical knowledge, the growing complexity of health data and analytical techniques demands further training beyond introductory levels. Practitioners and researchers are often required to engage with regression modelling, survival analysis, prediction models, and real-world evidence generated from routinely collected data, yet opportunities for structured, practice-oriented training in these methods remain limited.
This 12-week certificate course addresses this gap by providing a comprehensive and practical grounding in advanced statistical and analytical methods applied to community health research, enabling participants to move confidently from data analysis to evidence-informed practice.
Objectives
At the completion of this course, participants will be able to:- Formulate clear, impactful, and feasible quantitative research questions in community health care.
- Select and justify appropriate quantitative study designs for health research.
- Apply foundational and advanced concepts in epidemiology and biostatistics to research problems.
- Critically appraise published quantitative research for methodological quality and validity.
- Understand ethical, logistical, and organisational requirements of conducting research.
- Interpret and translate quantitative research findings to inform clinical practice, health services planning, and policy decision-making.
Content & Schedule
Course Content
Twelve sessions will be held on Tuesday evenings (7:00-9:00pm) between January and April 2027.
| Session | Date | Topic | Speaker |
| 1 | 19 Jan |
Statistical Inference Foundations
|
Prof. Francisco Lai |
| 2 | 26 Jan |
Real-World Evidence: Design
|
Prof. Ching Lung Cheung |
| 3 | 2 Feb |
Real-World Evidence: Analysis
|
Prof. Ching Lung Cheung |
| 4 | 16 Feb |
Parametric and Non-parametric Comparisons
|
Dr. Yuk Kam Yau |
| 5 | 23 Feb |
Linear and Logistic Regression
|
Prof. Eric Wan |
| 6 | 2 Mar |
Survival and Time-to-Event Analysis
|
Prof. Eric Wan |
| 7 | 23 Mar |
Screening and Prediction Performance
|
Dr. April Luo |
| 8 | 30 Mar |
Machine Learning I: Core Concepts
|
Prof. Jiandong Zhou |
| 9 | 6 Apr |
Psychometrics and Factor Analysis
|
Prof. Francisco Lai |
| 10 | 13 Apr |
Machine Learning II: Applications
|
Prof. Jiandong Zhou |
| 11 | 20 Apr | Student project presentations & Concluding summary of course | Students; Prof. Francisco Lai and Prof. Eric Wan |
| 12 | 27 Apr | Class quiz | Students; Prof. Francisco Lai and Prof. Eric Wan |
Schedule
A minimum attendance of 80% of the sessions is required to pass the Course. Student absence due to illness requires a registered doctor’s certificate. The seminars and workshops will be provided to students with videos or user manual.
The attendance policy for this course encourages in-person participation, while allowing students to attend up to 2 classes via Zoom. Pre-registration is required for Zoom attendance, and up to 20% of enrolled participants may attend remotely for each session.
Venue
Centennial Campus, Central Podium Levels - Lower Ground (CPD-LG.61, Cheng Yu Tung Tower)
Language of Instruction
English
Awards & Accreditations
Award of Certificate
The Certificate in Advanced Statistical Methods and Technology in Community Health Care will be awarded to participants who have enrolled in the full course, attended at least 80% of the sessions, and achieved a satisfactory mark on all course assessments. Student absence due to illness requires a registered doctor’s certificate.
The attendance policy encourages in-person participation, while allowing students to attend up to 2 sessions via Zoom. Pre-registration is required for Zoom attendance.
Course Assessment
Continuous Assessment (100%)
1. Coding Assignment: Linear and Logistic Regression in R (30% of total mark)
An individual hands-on assignment requiring students to apply linear and logistic regression to a community health research scenario using R. Students will conduct data preparation, model fitting, and result interpretation in a clinically relevant context.
2. Class Quiz (20% of total mark)
A final quiz will be conducted to assess key statistical concepts covered throughout the course. The quiz will focus on interpretation and appropriate method selection rather than numerical calculation.
3. Group Project: Paper Critique of Methods and Results (50% of total mark)
A group-based critical appraisal of a published scientific manuscript relevant to community or primary health care. Students will critique the study design, statistical methods, and presentation of results, and discuss implications for clinical practice and policy.
Passing mark is 60% overall.
CME Accreditations
We plan to apply for CME, CNE, CPD, and CPE accreditation from the respective professional associations where applicable.
People
Coordinator
Prof. Eric Wan
Associate Professor, Department of Family Medicine and Primary Care & Department of Pharmacology and Pharmacy, The University of Hong Kong
Prof. Francisco Lai
Assistant Professor, Department of Pharmacology and Pharmacy, The University of Hong Kong
List of Speakers
Prof. Ching Lung Cheung
Associate Professor, Department of Pharmacology and Pharmacy, The University of Hong Kong
Dr. Yuk Kam Yau
Postdoctoral Fellow, Department of Family Medicine and Primary Care, The University of Hong Kong
Dr. April Luo
Research Assistant Professor, Department of Family Medicine and Primary Care, The University of Hong Kong
Prof Jiandong Zhou
Assistant Professor, Department of Family Medicine and Primary Care & Department of Pharmacology and Pharmacy, The University of Hong Kong
