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Understanding Clinical Research: Behind the Statistics

Approx. 27 hours to complete

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Course Summary

This course provides an overview of clinical research and its importance in healthcare. Students will learn about the design and conduct of clinical trials, ethical and regulatory considerations, and data analysis.

Key Learning Points

Understand the fundamentals of clinical research
Learn about the design and conduct of clinical trials
Gain knowledge of ethical and regulatory considerations
Explore data analysis in clinical research

Job Positions & Salaries of people who have taken this course might have

Clinical Research Coordinator
- USA: $50,000 - $80,000
- India: INR 2,50,000 - INR 7,00,000
- Spain: €20,000 - €45,000
Clinical Data Manager
- USA: $60,000 - $110,000
- India: INR 3,00,000 - INR 12,00,000
- Spain: €30,000 - €60,000
Clinical Research Associate
- USA: $70,000 - $120,000
- India: INR 4,50,000 - INR 15,00,000
- Spain: €35,000 - €70,000

Learning Outcomes

Understand the importance of clinical research in healthcare
Be able to design and conduct clinical trials
Gain knowledge of ethical and regulatory considerations in clinical research

Prerequisites or good to have knowledge before taking this course

Basic knowledge of healthcare and medical terminology
Familiarity with basic statistical concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Quizzes and assignments

Similar Courses

Introduction to Biostatistics
Clinical Trials: Design and Analysis

Related Education Paths

Notable People in This Field

Dr. Sanjay Gupta
Dr. Anthony Fauci

Related Books

Description

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.

If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started! The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

Outline

Getting things started by defining study types
Introduction to Understanding Clinical Research
About the course
Observing and intervening: Observational & experimental studies
Observing and describing: Case series studies
Comparing groups: Case-control studies
Collecting data at one point in time: Cross-sectional studies
Studying a group with common traits: Cohort studies
Let's intervene: Experimental studies
Working with existing research: Meta-analysis and Systematic Review
Doing a literature search: Part 1
Doing a literature search: Part 2
How this course works
Pre-course survey
Study types
Key notes: Observational and experimental studies
Key notes: Case series studies
Key notes: Case-control studies
Key notes: Cross-sectional studies
Key notes: Cohort studies
Key notes: Experimental studies
Key notes: Meta-analysis and systematic review
Peer review introduction
Test your knowledge: Study types

Describing your data
Introduction
Some key concepts: Definitions
Data types
Arbitary classification: Nominal categorical data
Natural ordering of attributes: Ordinal categorical data
Measurements and numbers: Numerical data types
How to tell the difference: Discrete and continuous variables
Introduction
Measures of central tendency
Measures of dispersion
(Optional) Setting up spreadsheets to do your own analysis
(Optional) Descriptive statistics using spreadsheets
Making inferences: Sampling
Types of sampling
Case study 1
Key notes: Definitions
Key notes: Data types
Key notes: Nominal categorical data
Key notes: Ordinal categorical data
Key notes: Numerical data types
Key notes: Discrete and continuous variables
Key notes: Describing the data
Key notes: Measures of central tendency
Key notes: Measures of dispersion
Visual representation of data
Key notes: Sampling
Key notes: Types of sampling
Test your knowledge: Data types
Test your knowledge: Measures of central tendency and dispersion
Test your knowledge: Sampling
Week 2 Graded Quiz

Building an intuitive understanding of statistical analysis
P-values: P is for probability
Working out the probability: Rolling dice
Area under the curve: Continuous data types
Introduction to the central limit theorem: The heart of probability theory
Asymmetry and peakedness: Skewness and Kurtosis
Learning from the lotto: Combinations
Approximating a bell-shaped curve: The central limit theorem
Patterns in the data: Distributions
The bell-shaped curve: Normal distribution
Plotting a sample statistic: Sampling distribution
Standard normal distribution: Z distribution
Estimating population parameters: t-distribution
(Optional) Generating random data point values using spreadsheet software
Case study 2
Key notes: P-values
Key notes: Rolling dice
Key notes: Continuous data types
Introduction to the central limit theorem
Key notes: Skewness and kurtosis
Key notes: Combinations
Key notes: Central limit theorem
Key notes: Distributions
Key notes: Normal distribution
Key notes: Sampling distribution
Key notes: Z-distribution
Key notes: The t-distibution
Test your knowledge: Probability
Test your knowledge: The central limit theorem
Test your knowledge: Distributions
Week 3 Graded Quiz

The important first steps: Hypothesis testing and confidence levels
Introduction to Hypothesis Testing
Testing assumptions: Null and alternative hypothesis
Is there a difference?: Alternative Hypothesis
Type I and II: Hypothesis testing errors
Introduction to confidence intervals
How confident are you?: Confidence levels
Interval estimation: Confidence intervals
(Optional) Calculating confidence intervals using spreadsheet software
Key notes: Null and alternative hypothesis
Key notes: Alternative hypothesis
Key notes: Hypothesis errors
Key notes: Introduction to confidence intervals
Key notes: Confidence levels
Key notes: Confidence intervals
Testing your knowledge: Hypothesis
Test your knowledge: Confidence intervals

Which test should you use?
Introduction to parametric tests
Student's t-test
ANOVA
Linear Regression
(Optional) Student's t-test in action
Introduction to nonparametric tests
Checking for normality
Thinking nonparametrically
Comparing paired observations: Signs
Ordering values: Ranking
Paired comparisons: Sign ranks
Summation of ranks: Rank sums
Comparing two populations: Mann-Whitney-U test
More nonparametric tests
Case study 3
Key notes: Parametric tests
Key notes: Student's t-test
Key notes: ANOVA
Key notes: Linear regression
Key notes: Nonparametric tests
Key notes: Nonparametric tests
Test your knowledge: Parametric tests
Test your knowledge: Non-parametric tests
Week 5 Graded Quiz

Categorical data and analyzing accuracy of results
Introduction to comparing categorical data
Observed frequencies: Contingency tables
Comparing observed and expected values: Chi-square test
Association between two variables: Fisher's exact test
(Optional) Calculating chi-square test using spreadsheet software
Introduction to sensitivity and specificity
Measuring performance: Sensitivity and specificity
Proportions of results: Positive and negative predictive values
Introdution to risk and odds ratios
Risk and odds ratios - Losses (Risk)
Risk and odds ratios - Losses (Odds)
Risk and odds ratios - Wins
Risk and odds ratios example
Key notes: Comparing categorical data
Keynotes: Sensitivity, specificity, positive and negative predictive values
Congratulations on completing the course
Risk and odds ratios
Testing your knowledge: Comparing categorical data
Test your knowledge: Sensitivity, specificity and predictive values
Week 6 Final examination

Summary of User Reviews

Clinical Research course on Coursera has received positive reviews from users. The course has been praised for its comprehensive curriculum, interactive exercises, and knowledgeable instructors. Many users have found the course to be informative and engaging, with practical insights into the field of clinical research.

Key Aspect Users Liked About This Course

The course's comprehensive curriculum has been praised by many users.

Pros from User Reviews

Informative and engaging course material
Interactive exercises that facilitate learning
Knowledgeable and responsive instructors
Practical insights into the field of clinical research

Cons from User Reviews

Some users felt that the course could have been more challenging
A few users reported technical difficulties with the platform
The course may not be suitable for those with no prior experience in clinical research

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