• Length:
    6 Weeks
  • Effort:
    3–5 hours per week
  • Price:

    Add a Verified Certificate for $49 USD

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  • Course Type:
    Self-paced on your time

About this course

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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

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  • Important topics in medical statistical concepts and reasoning
  • Epidemiological Study Designs
  • Data analysis using R Commander
Week 1 Basic Statistical Concepts
Introduction 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 your instructors

Ayumi Shintani
Endowed Professor, Chair of Department of Clinical Epidemiology and Biostatistics
Osaka University Graduate School of Medicine

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