Introduction to Biostatistics: Basic for Public Health

Part 1: Course Information

Instructor Information

Instructor                : Professor dr. Siswanto Agus Wilopo, S.U, M.Sc., Sc.D.

Office                        : Gedung IKM Room 001, Jl. Farmaco 1, Bulaksumur, Yogyakarta
Office Hours           : 08.0-10.00 & Monday and Wednesday
Office Telephone : 62-274-548-156 or 5656076
E-mail                       : sawilopo@ugm.ac.id

Teaching Assistant

Coordinator            : dr. Amirah Ellyza Wahdi, MSPH

Office                         : Gedung IKM Room 008, Jl. Farmaco 1, Bulaksumur, Yogyakarta
Office Hours            : 08.0-10.00 & Tuesday and Thursday
Office Telephone  : 62-274-548-156 or 5656076
E-mail                        : amirahellyzawahdi@ugm.ac.id

Course Description

Biostatistics is the development and application of statistical reasoning and methods in addressing, analyzing, and solving problems in public health, health care, and biomedical, clinical, and population-based research. This course will cover descriptive statistics, probability theory, and a wide variety of inferential statistical techniques that can be used to make practical conclusions about empirical data derived from public health works. We will use two approaches to the mastery of the course materials: a) students will look in some detail at how statistical procedures are employed and b) students will conduct several basic procedures by exercising with real data to fully understand the logic and application of statistics.  In addition, the student will learn how to use a computer package, Stata, to quickly perform statistical analyses in more complex situations and how to interpret the results of the data analyses. Using this approach will enable students to be educated users and producers of statistical knowledge in the real world, which is the application of statistics in public health and medical settings. This course is mandatory for Graduate and MPH students.  

Prerequisite

Students should be familiar with basic mathematical calculation, probability, and the use of a computer.

Textbook & Course Materials

Required Text

1.  Pagano, M. Gauvreau, K. and Mattie, H (2022). Principles of Biostatistics. Third Edition CRC Press:  6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
2.  Rosner B (2016). Fundamentals of Biostatistics, 8th ed. California: Cengage Learning pp:1-888.
3. Copies of the lecture notes (pdf) may be obtained at the Web Course (Elok or Gamel).

Recommended Texts & Other Readings

1. Etzioni, Ruth; Mandel, Micha; Gulati, Roman (2021). Statistics for Health Data Science An Organic Approach. Gewerbestrasse 11, 6330 Cham, Switzerland: Springer Nature Switzerland AG.

Course Requirements                                                       

The course requirements are intended to help students understand and apply for the course material. The weekly discussion with the teaching assistant will test students’ understanding of the readings. The written assignments focus on the application of the course material to specific health development projects. Students should upload each assignment to the appropriate Elok discussion thread by the deadlines posted. The student should have the following:

• Internet connection (DSL, LAN, or cable connection desirable).
• Access to Website/Other
• Software Stata which can be accessed at the Medical Library.

Course Structure

This course will be delivered in lectures and discussions during laboratory sessions using an online and hybrid approach (when it is possible). The lecture will be given twice weekly or depending on the department’s schedule. In each topic, the lecturer will present statistical theory or concepts and will discuss them within the class. The instructor will assign the exercises (homework) at the end of each laboratory session as stated in the lecture module. Exercises reinforce quantitative skills and specific content areas. During the course, students will experience and apply their knowledge through the development of data analysis given in the exercise or laboratory module.

Enrolment

This course is open to a limited number of individuals outside of the MPH's programs.  Preference is given to UGM-affiliated students, including doctorate students. We regret that auditing is not permitted. To apply for this course please fill out and apply available at the study program. The cost and submission information is in the application form.

Lecture Topics

The lecture will cover statistical theories and their application to health research. It will be given at least twice a week (Tuesday and Thursday: 10.00-12.00). Each session is about 100 minutes. There will be 14 sessions of lectures during this class and 12 Laboratory modules.

Laboratory Exercise

Students will be allowed to explore further details of the lecture materials in the form of discussion and exercise in the class. The teaching assistant and computer programmer are assigned to lead this class discussion and exercise. Their tasks provide student’s better understanding of the lecture materials and problem sets for the previous homework.

Problem Sets for Homework

Problem sets require the use of Stata (or a comparable statistics package, such as R). You will need to submit a Stata code (or code from an equivalent package. If you are using Stata, the code is automatically generated for you as a log file. You need to cut and paste the relevant code from this automatically generated code into your homework (which will take some understanding of the code itself). 

Online Resources

All lecture and laboratory materials are distributed in the Web class. These resources are available in Elok or Gamel.   

Part 2: Student Learning Outcomes

Learning Objectives

This course will explain how to:

1. Formulate the proper statistical technique to be used in analyzing data sets
2. Apply statistical knowledge to designing research studies, including computing the sample sizes necessary to show the statistical significance and analytical techniques for the certain study design.
3. Analyze public health data with proper statistical techniques using a computer statistical software package (Stata).
4. Interpret computer outputs for the more commonly used statistical tests in the public health field.
5. Appraise public health and scientific journal articles that frequently rely heavily on statistical procedures, such as linear regression, logistic regression, and survival analysis.

Learning Outcomes

By the end of this course, students should be able to:

1. Describe the roles biostatistics serves in the discipline of public health.
2. Describe the basic concepts of probability, random variation, and commonly used statistical probability distributions.
3. Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
4. Distinguish among the different measurement scales and the implications for the selection of statistical methods to be used based on these distinctions.
5. Apply descriptive techniques commonly used to summarize public health data.
6. Apply common statistical methods for inference.
7. Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
8. Apply basic informatics techniques with vital statistics and public health records in the description of public health characteristics and public health research and evaluation.
9. Interpret results of statistical analyses found in public health studies.
10. Develop written and oral presentations based on statistical analyses for both public health professionals and educated lay audiences.

Epidemiology for public health provides concepts and basic principles of epidemiology for public health challenges. The main focuses of the course are the methods and principles of epidemiology investigation, summarise and present epidemiological data, and basic statistics application to describe population health. 

Specific topics included in the course:

1. Use of rates, ratio, and proportion for diseases frequency calculation, 

2. Direct and indirect adjustments methods 

3. Clinical life table for disease severity measurement and description 

Students will also learn various epidemiology study designs to investigate the association between risk factors and disease outcome.  In addition, in this course application of epidemiology in healthcare services, disease screening, genetic study, and environmental policy will also be introduced. 

The course is fully online with a combination of synchronous and asynchronous sessions. The majority of study material is in video format. Thus students can review the course material anytime. Tutorial sessions are synchronous, and students can discuss with tutors directly. 

Learning outcome

1. Students can explain the concept of epidemiology.
2. Students can conduct data analysis using a proper epidemiology method.

• 4 modules in one semester
  
• Each model consists of 2-4 unit