- Length: 6 weeks
- Effort: 3-5 hours/week
-
Price:
Free
Add a Verified Certificate for $49 - Institution: OsakaUx
- Subject: Data Analysis & Statistics
- Level: Intermediate
- Languages: English
- Video Transcripts: English
About this course
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 all homework assignments will expose
learner to 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 and PS sample size
software.
What you'll learn
- Important topics in medical statistical concepts and reasoning
- Epidemiological Study Designs
- Data analysis using R Commander
Course Syllabus
Week 1 Basic Statistical ConceptsIntroduction to basic statistical concepts, such as descriptive statistics, hypothesis testing, how to enter data in to statistical software and how to use easy R interface.
Week 2 Basic Epidemiological Concepts
Introduction to basic epidemiological concepts, such as study designs as well as the difference between observational studies and randomized clinical trials.
Week 3 Selecting Proper Statistical Tests
Students will learn how to select a proper statistical test, given scenarios defined by various data types.
Week 4 Student T-Test, Man-Whitney U Test, Paired T-test, Wilcoxon Signed Rank Test
Students will learn how to compare means or medians between two groups.
Week 5 Risk, Rate and Chi-Square Tests
Students will learn how to analyze binary outcome data.
Week 6 Sample Size and Power Analysis
Introduction to basic concepts in computing sample sizes and estimation power for clinical studies.
Meet the instructor
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Ayumi ShintaniEndowed Professor, Chair of Department of Clinical Epidemiology and Biostatistics Osaka University Graduate School of Medicine
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