Biostatistics Application for Health Data 2023-2024

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             : Drs. Althaf Setiawan, MPH

Office                          : Pusat Kesehatan Reproduksi, Jl. Mahoni 24, Bulaksumur, Yogyakarta
Office Hours             : 08.0-10.00 & Tuesday and Thursday
Office Telephone   : 62-0274-292-4789
E-mail                         : althaf@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 and clinical 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 actual data to fully understand the logic and application of statistics.  In this case, students will use varieties data derived from public health and clinical research. In addition, the student will learn how to use a computer package, STATA, to perform statistical analyses in more complex situations quickly 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 clinical settings. This course is mandatory for doctoral students in health sciences.          

Prerequisite

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

Textbook & Course Materials

Required Text

1. Rosner B (2016). Fundamentals of Biostatistics, 8th ed. California: Cengage Learning pp:1-888.
2. 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
3. Peacock JL & Peacock PJ (20120) Oxford Handbook of Medical Statistics. Second Edition. Oxford University Press, London.
4. Copies of the lecture notes (pdf) may be obtained at the Web Course (Elok or Gamel).

Recommended Texts & Other Readings

1.  Kros, John F.; Rosenthal, David A.; Veney, James E (2016).  Statistics for health care management and administration: working with Excel. 3^rd Edition. New York: John Wiley & Sons.

Course Requirements                                                                

The course requirements are intended to help students understand and apply 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:

1. Internet connection (DSL, LAN, or cable connection desirable).
2. Access to Web site/Other
3. Software Stata which can be accessed at Medica 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 an 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 (e.g., exploratory data analyses or EDA) for continuous or count data, parametric or non-parametric methods, and independent or paired samples).
2. Apply statistical knowledge to designing research studies. This includes computing the sample sizes necessary to show the statistical significance and selecting the proper 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.

Part 3: Grading Policy

Graded Course Activities

Final grades assigned for this course will be based on the percentage of total points earned and are assigned as follows:

Part 4: Course Policies

Attend Class

Students are expected to attend all class sessions and laboratory exercises as listed on the course calendar. If a student attends less than 75% of class or laboratory exercises is not given a final grade.

Participate

The Laboratory session must be attended by all students. The student will be divided into groups and is randomly selected by the Teaching assistant coordinator. The teaching assistant will record your class activities as part of your grade.

Build Rapport

If you find that you have any trouble keeping up with assignments or other aspects of the course, make sure you let your instructor know as early as possible. As you will find, building rapport and effective relationships are key to becoming an effective professional. Make sure that you are proactive in informing your instructor when difficulties arise during the semester so that they can help you find a solution.

Completed Assignments

All assignments for this course will be submitted electronically through Elok unless otherwise instructed. Assignments must be submitted by the given deadline or special permission must be requested from the instructor before the due date. Extensions will not be given beyond the next assignment except under extreme circumstances. All discussion assignments must be completed by the assignment's due date and time. Late or missing discussion assignments will affect the student’s grade.

Understand When You May Drop This Course

It is the student’s responsibility to understand when they need to consider disenrolling from a course. Refer to the dates and deadlines for registration. After this period, a serious and compelling reason is required to drop from the course.

Incomplete Grade Policy

Under emergency/special circumstances, students may petition for an incomplete grade. An incomplete will only be assigned if there is an acceptable reason, for example, due to sickness. All incomplete course assignments must be completed within semester 1.

Commit to Integrity

As a student in this course (and at this university) you are expected to maintain high degrees of professionalism, commitment to active learning and participation in this class, and also integrity in your behavior in and out of the classroom.

Academic Dishonesty Policy

1. Academic dishonesty includes such things as cheating, inventing false information or citations, plagiarism, and helping someone else commit an act of academic dishonesty. It usually involves an attempt by a student to show possession of a level of knowledge or skill that he/she does not possess.
2. Course instructors have the initial responsibility for detecting and dealing with academic dishonesty. Instructors who believe that an act of academic dishonesty has occurred are obligated to discuss the matter with the student(s) involved. Instructors should possess reasonable evidence of academic dishonesty. However, if circumstances prevent consultation with student(s), instructors may take whatever action (subject to student appeal) they deem appropriate.
3. Instructors who are convinced by the evidence that a student is guilty of academic dishonesty shall assign an appropriate academic penalty. If the instructors believe that academic dishonesty reflects on the student's academic performance or the academic integrity in a course, the student's grade should be adversely affected. Suggested guidelines for appropriate actions are: -an oral reprimand in cases where there is reasonable doubt that the student knew his/her action constituted academic dishonesty; -a failing grade on a particular paper, project, or examination where the act of dishonesty was unpremeditated, or where there were significant mitigating circumstances; -a failing grade in the course where the dishonesty was premeditated or planned.
4. The instructors will file incident reports with the Dean of Academic Affairs and for Student Affairs or their designees. These reports shall include a description of the alleged incident of academic dishonesty, any relevant documentation, and any recommendations for action that he/she deems appropriate.

A Health Technology Assessment (HTA) course at the doctorate degree level is a specialised program designed to equip students with advanced knowledge and skills in the field of HTA. This course typically delves deep into the principles, methodologies, and applications of HTA, preparing students to become experts in evaluating healthcare technologies and interventions. After completing this course, students are expected to have a deep understanding of the fundamental principles, modeling techniques, and the application of Health Technology Assessment (HTA) in the context of healthcare services