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22 Kursus

Pengajar: Dr. ARIF SABTA AJI, S. Gz. Dosen Universitas Alma AtaPengajar: NUR AINI KUSMAYANTI, S.Gz. Dosen Universitas Alma AtaPengajar: Prof. dr. Siswanto Agus Wilopo, SU, M.Sc, Sc.D Dosen Universitas Alma AtaPengajar: YHONA PARATMANITYA, S.Gz., Dietisien., M.P.H. Dosen Universitas Alma Ata

Biostatistics II: Multivariable Analysis

Course Description

Modern multivariable statistical analysis based on the concept of generalized linear models which includes linear, logistic, and Poisson regression, survival analysis, fixed-effects analysis of variance and repeated measures analysis of variance. This course emphasizes the underlying similarity of these methods, the choice of the right method for specific problems, common aspects of model construction, the testing of model assumptions through influence and residual analyses, and the use of graphical and other methods to present results that are readily understood by health researchers. This is a second course in biostatistics, covering multi-predictor methods, including exploratory data analysis and multiple regressions (linear and logistic). This course will cover more details on categorical data (logistic and log linear modeling) and survival analysis (time to event issues). In addition, the new topics will be introduced: fixed effect analysis of variance (anova), mixed effect of analysis of variance, marginal effects, structural equation modelling and causal inferences. Emphasis is on the practical and proper use of statistical methodology and its interpretation. The statistics package STATA will be used throughout the course. Student interests on analyzing a big data set (i.e. IDHS or SUSENAS) they are suggested to take course.

Goals and Course Objectives

The goal of this course is providing knowledge and skill of the students for analyzing of data using a multivariable technique. At the end of the course, students will be able to:

  1. compare the roles of descriptive versus inferential statistics,
  2. assess characteristics of the problem to help choose the appropriate analytic technique,
  3. compare techniques appropriate for handling a single outcome variable and multiple predictors,
  4. evaluate data limitations and their consequences,
  5. demonstrate analysis of binary data using a multivariable analysis,
  6. perform analysis of time to event data (survival analysis) with time dependent covariate,
  7. describe analysis parametric survival analysis,
  8. conduct simple and multiple poison regressions,
  9. perform analysis with missing data,
  10. analysis data using marginal effects, and
  11. demonstrate to use structural equation modelling, and
  12. evaluate data on causal inferences.


Prerequisites         

Passed Introduction Biostatistics I: Basic for Public Health (KUI-6611) and evidence of knowledge of the use of STATA are required. Exceptions to these prerequisites may be made with the consent of the course coordinator if space permitting.


Enrollment

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 submit the application available at the study program. Cost and submission information are in the application form.


Lecture Topics

Lecture will cover statistical theories and its application for health research. It will be given at least twice a week. Each session is about 100 minutes. There will be 14 sessions of lectures during this class (see following Table Class Calendar: 2020-2021). 


Laboratory Exercise

Students will be given opportunity 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 on 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 STATA code (or code from an equivalent package. If you are using STATA, the code is automatically generated for you as a logfile. 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). 


Assignments/Grading

6 Graded Problem Sets………… 60% (late policy: 10% deduction per day late)

In-Class Final Examination...…….. 40%

 (All assignment and exam should be submitted in electronics form to avoid plagiarism. Student who conducts plagiarism will not be given grade and she/he has to retake similar class next year).


Required Readings

Chapters to read for this course will be available in printed matter in the class.

  1. Dupont WD (2009). Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data. 2nd Ed.  Cambridge: Cambridge University Press. Referred to as “DUPONT” in this course syllabus.
  1. Vittinghoff, E., Glidden DV, Shiboski, SC, McCulloch, ME (2012). Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models, second edition. New York: Springer. Referred to as “VGSM” in this course syllabus.
  2. Mitchell, N.M. (2021). Interpretating and Visualizing Regression Model Using Stata. Lakeway Drive, College Station, Texas: Stata Press.

 

Suggested Readings
  1. Afifi, Abdelmonem; May, Susanne; Donatello, Robin A.; Clark, Virginia A (2020). Practical Multivariate Analysis. Sixth Edition. Boca Raton, FL, USA: CRC Press Taylor & Francis Group
  2. Breslow, N. E.  & DAY, N. E.  (1980). Statistical Methods in Cancer Research Volume 1 - The analysis of case-control studies. IARC, Lyon, France.
  3. Garson, Davis (2014). Logistic Regression: Binary and Multinomial. Asheboro, NC 27205 USA: G. David Garson and Statistical Associates Publishing.
  4. Harrell, FE. Jr. (2015).  Regression with Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.  New York: Springer. Referred to as “RMS” in this course syllabus.