MED101x
Introduction to Applied Biostatistics: Statistics for Medical Research- Part 1
ACADEMIC POLICY
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GETTING HELP
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BRIEF DESCRIPTION
Learn data analysis for medical research with practical hands-on examples using R Commander.
COURSE OVERVIEW
Want to learn how to analyze real-world medical data, but unsure where to begin? This Applied Biostatistics course provides an introduction to important topics in medical statistical concepts and reasoning. Each topic will be introduced with examples from published clinical research papers and hands-on data analysis using real-life datasets. This course also represents an introduction to basic epidemiological concepts covering study designs and sample size computation. Open-source, easy-to-use software will be used, such as R commander (Rcmdr), Easy R (EZR), and Power and Sample Size Calculation (PS) software.
PREREQUISITES
None
COURSE LEVEL
Introductory to intermediate
LEARNING OUTCOMES
Student will be able to….
- Understand basic statistical concepts in medical field
- Select methodology of statistical testing correctly along with study design in medical area
- Practice univariate analysis with using statistical software
- Interpret results of statistical analysis to be used in a real-life medical application
ASSESSMENTS (GRADING)
In order to get a certificate for the successful completion of this course, you need to earn cumulative grade of 60% or higher. Also you need to submit all graded quizzes (Weekly Assignment quizzes) and a final exam before a deadline. The deadline for submitting all the Weekly Assignment quizzes and Final Exam is March 1 March 15, 2016 UTC 23:59 (UTC time converter). Also we will announce our weekly updates on the Course Info page. Please check it out regularly, especially when new course contents are released.
Here is how your final grading will be done:
- (WEEKLY) QUIZZES - 60%
There will be Weekly Assignment quizzes at the end of each week lecture. The quizzes would promote your understanding and reflection. If you have participated in the class, the points should be automatic.
All the weekly assignments submission is due at 23:59 UTC, March 1 March 15, 2016. - FINAL EXAM - 40%
There will be a final test at the end of the course by multiple-choice or cloze test. Final test is to confirm your understanding. Many questions of the final test are from previously covered questions.
The submission is due at 23:59 UTC, March 1 March 15, 2016.
WORKLOAD
We anticipate students spending 3-5 hours per week, depending on background.
COURSE SCHEDULE
| Week/Lecture Title | Release Date/Time | Quizzes Due Dates |
|---|---|---|
| Week 1 Basic Statistical Concepts |
January 19, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
| Week 2 Basic Epidemiological Concepts |
January 26, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
| Week 3 Selecting Proper Statistical Tests |
February 2, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
| Week 4 Student t-test, Mann-Whitney U test, Paired t-test, Wilcoxon signed-rank test |
February 9, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
| Brush-up Week Mid-Couse Survey |
February 16, 2016 05:00 UTC (14:00 JST) |
March 15, 2016 23:59 UTC |
| Week 5 RISK, RATE and Chi-square tests |
February 16 February 23, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
| Week 6 Sample size and power analysis |
February 23 March 1, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
| Final Exam | February 23 March 1, 2016 05:00 UTC (14:00 JST) |
March 1 March 15, 2016 23:59 UTC |
WEEK 1 - Basic Statistical Concepts
Introduction to basic statistical concepts such as descriptive statistics including mean, standard deviation, median, inter-quartile range, hypothesis testing, concepts of p-values and confidence intervals, how to enter data in to statistical software, and how to use R commander and EZR interface using read data set from Titanic.
WEEK 2 - Basic Epidemiological Concepts
In order to understand medical statistics, it is essential to learn basic epidemiological concepts. You will be learning difference between experimental studies vs. observational studies, cohort studies, case-control, and cross-sectional studies. Concepts of randomization in clinical trials will be discussed with biases introduced in non-randomized studies.
WEEK 3 - Selecting Proper Statistical Tests
Students will learn how to select a proper statistical test given various scenarios defined by the following conditions (1) randomized vs observational studies, (2) detecting difference or correlation, (3) data dependence or independence, (4) outcome data type, (5) distribution of outcome data, (6) number of comparison groups in detecting differences (7) sample size.
WEEK 4 - Student's t-test, Mann-Whitney U test, Paired t-test, Wilcoxon signed-rank test
Students will learn how to compare means of continuous outcome variables between two independent groups by using Student’s t-test and comparing medians of continuous outcome variables by using Mann-Whitney U test. Paired t-test and Wilcoxon signed-rank test will be also covered to compare means (or medians) of continuous outcome variable between two related groups.
WEEK 5 - Risk, Rate and Chi-square tests
When we compare a proportion of having an event (for example heart attack) in two groups of patients, ratio of two proportions provides relative risk (RR) as a measure of association between an exposure and an outcome. A similar matrix includes odds ratio (OR). In this chapter, students will learn how to compute and interpret RR and OR with a hands-on EZR computation using read data from Framingham heart study.
WEEK 6 - Sample Size and Power Analysis
When we plan a clinical study, it is highly important to estimate at least how many numbers of patients are needed in order to reach an estimated effect of an exposure of interest. Students will learn basic concepts in computing sample sizes clinical studies using PS software.
COMMUNICATION / DISCUSSION FORUM
Weekly updates will be posted on the Course Info page regularly. For course-related communications, we encourage you to actively participate in the discussion forum. Please use the discussion forum to ask questions about concepts from lectures and Quizzes. Your posts will be monitored by course staff once a week (Mondays); however, owing to the large number of students and limited resources, we may not be able to answer all questions. Therefore, we encourage students to answer each other’s questions and upvote helpful answers. The course instructors will not be posting in the discussions. We would appreciate your understanding.
If you have any question regarding the grading policy, you can post it on discussion forum under Technical Problems section. For some useful tips on how to navigate the discussion forum, please see the Forum Guidelines and Technical Problems Guidelines.
THE DISCUSSION FORUM WILL BE MODERATED.
Defamatory comment should not be posted on the discussion forum. When we have found any such comments, we will delete them without permission. In addition, if you find any defamatory comments, please red flag posts on the discussion forum. Our team can get involved as soon as possible.
MED101x TEAM
INSTRUCTOR COURSE STAFF
Haruo Takemura (Teaching and Learning Support Center, Osaka University)
Kumud B. Singh (Teaching and Learning Support Center, Osaka University)
Chie Gondo (Teaching and Learning Support Center, Osaka University)
Makiko Oyama (Teaching and Learning Support Center, Osaka University)
TEACHING ASSISTANT
Yi Zhou (Graduate School of Medicine, Osaka University)
Sekai Katsumi (Graduate School of Human Sciences, Osaka University)
Kojiro Ota (Graduate School of Engineering Science, Osaka University)
Parisa Mehran (Graduate School of Language and Culture, Osaka University)
Mehrasa Alizadeh (Graduate School of Language and Culture, Osaka University)
STUDENT STAFF
Hikari Okada (School of Human Sciences, Osaka University)
Takumi Mochida (School of Letters, Osaka University)
Neneka Nakamura (School of Foreign Studies, Osaka University)
Nana Sato (School of Foreign Studies, Osaka University)
Akito Kunihiro (School of Engineering Science, Osaka University)