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Introduction to Applied Biostatistics: Statistics for Medical Research

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Free Online Course: Introduction to Applied Biostatistics: Statistics for Medical Research provided by edX is a comprehensive online course, which lasts for 6 weeks long, 3-5 hours a week. The course is taught in English and is free of charge. Introduction to Applied Biostatistics: Statistics for Medical Research is taught by Ayumi Shintani.

Handai via edX
6 weeks long, 3-5 hours a week
Self Paced
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 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.

Syllabus

  • 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.

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