About this course: 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.
Created by: University of Cape Town
Taught by: Juan H Klopper, Dr
Department of Surgery
| Commitment | 6 weeks of study, 2-3 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Getting things started by defining study types
Welcome to the first week of this course. We’ll be tackling five broad topics to provide you with an intuitive understanding of clinical research results. This isn’t a comprehensive statistics course, but it offers a practical orientation to the field of medical research and commonly used statistical analysis. The first topics will look at research methods and the collection of data - with a specific focus on study types. By the end of the lectures you should be able to identify which study types are being used and why the researchers selected them when you are reading a paper.
11 videos, 11 readings, 1 practice quiz
- Video: Introduction to Understanding Clinical Research
- Video: About the course
- Reading: How this course works
- Reading: Pre-course survey
- Discussion Prompt: Introduce yourself to your peers
- Reading: Study types
- Video: Observing and intervening: Observational & experimental studies
- Reading: Key notes: Observational and experimental studies
- Video: Observing and describing: Case series studies
- Reading: Key notes: Case series studies
- Video: Comparing groups: Case-control studies
- Reading: Key notes: Case-control studies
- Video: Collecting data at one point in time: Cross-sectional studies
- Reading: Key notes: Cross-sectional studies
- Video: Studying a group with common traits: Cohort studies
- Reading: Key notes: Cohort studies
- Video: Let's intervene: Experimental studies
- Reading: Key notes: Experimental studies
- Video: Working with existing research: Meta-analysis and Systematic Review
- Reading: Key notes: Meta-analysis and systematic review
- Practice Quiz: Test your knowledge: Study types
- Reading: Peer review introduction
- Video: Doing a literature search: Part 1
- Video: Doing a literature search: Part 2
Graded: Week 1: Navigating Clinical Research
WEEK 2
Describing your data
With the next topics, we finally get started with the statistics. Have you ever looked at the methods and results section of any healthcare research publication and noted the variety of statistical tests used? You would have come across terms like t-test, Mann-Whitney-U test, Wilcoxon test, Fisher’s exact test and the ubiquitous chi-squared test. Why so many tests you might wonder? It’s all about types of data. In this week, I am going to tackle the differences in data which determine what type of statistical test we can use in making sense of our data.
15 videos, 12 readings, 3 practice quizzes
- Video: Introduction
- Video: Some key concepts: Definitions
- Reading: Key notes: Definitions
- Video: Data types
- Reading: Key notes: Data types
- Video: Arbitary classification: Nominal categorical data
- Reading: Key notes: Nominal categorical data
- Video: Natural ordering of attributes: Ordinal categorical data
- Reading: Key notes: Ordinal categorical data
- Video: Measurements and numbers: Numerical data types
- Reading: Key notes: Numerical data types
- Video: How to tell the difference: Discrete and continuous variables
- Reading: Key notes: Discrete and continuous variables
- Practice Quiz: Test your knowledge: Data types
- Video: Introduction
- Reading: Key notes: Describing the data
- Video: Measures of central tendency
- Reading: Key notes: Measures of central tendency
- Video: Measures of dispersion
- Reading: Key notes: Measures of dispersion
- Reading: Visual representation of data
- Video: (Optional) Setting up spreadsheet software to do your own analysis
- Video: (Optional) Descriptive statistics using spreadsheet software
- Practice Quiz: Test your knowledge: Measures of central tendency and dispersion
- Video: Making inferences: Sampling
- Reading: Key notes: Sampling
- Video: Types of sampling
- Reading: Key notes: Types of sampling
- Practice Quiz: Test your knowledge: Sampling
- Video: Case study 1
- Discussion Prompt: Share an example of clinical research
Graded: Week 2 Graded Quiz
WEEK 3
Building an intuitive understanding of statistical analysis
There is hardly any healthcare professional who is unfamiliar with the p-value. It is usually understood to have a watershed value of 0.05. If a research question is evaluated through the collection of data points and statistical analysis reveals a value less that 0.05, we accept this a proof that some significant difference was found, at least statistically.In reality things are a bit more complicated than that. The literature is currently full of questions about the ubiquitous p-vale and why it is not the panacea many of us have used it as. During this week you will develop an intuitive understanding of concept of a p-value. From there, I'll move on to the heart of probability theory, the Central Limit Theorem and data distribution.
14 videos, 12 readings, 3 practice quizzes
- Video: P-values: P is for probability
- Reading: Key notes: P-values
- Video: Working out the probability: Rolling dice
- Reading: Key notes: Rolling dice
- Video: Area under the curve: Continuous data types
- Reading: Key notes: Continuous data types
- Practice Quiz: Test your knowledge: Probability
- Video: Introduction to the central limit theorem: The heart of probability theory
- Reading: Introduction to the central limit theorem
- Video: Asymmetry and peakedness: Skewness and Kurtosis
- Reading: Key notes: Skewness and kurtosis
- Video: Learning from the lotto: Combinations
- Reading: Key notes: Combinations
- Video: Approximating a bell-shaped curve: The central limit theorem
- Reading: Key notes: Central limit theorem
- Practice Quiz: Test your knowledge: The central limit theorem
- Video: Patterns in the data: Distributions
- Reading: Key notes: Distributions
- Video: The bell-shaped curve: Normal distribution
- Reading: Key notes: Normal distribution
- Video: Plotting a sample statistic: Sampling distribution
- Reading: Key notes: Sampling distribution
- Video: Standard normal distribution: Z distribution
- Reading: Key notes: Z-distribution
- Video: Estimating population parameters: t-distribution
- Reading: Key notes: The t-distibution
- Video: (Optional) Generating random data point values using spreadsheet software
- Practice Quiz: Test your knowledge: Distributions
- Video: Case study 2
Graded: Week 3 Graded Quiz
WEEK 4
The important first steps: Hypothesis testing and confidence levels
In general, a researcher has a question in mind that he or she needs an answer to. Everyone might have an opinion on the question (or answer), but an investigator looks for the answer by designing an experiment and investigating the outcome. In the first lesson we will look at hypotheses and how they relate to ethical and unbiased research and reporting.We'll also tackle Confidence intervals which I believe are one of the least understood and often misrepresented values in healthcare research. The most common tests used in the literature to compare numerical data point values are t-tests, analysis of variance, and linear regression. In the last lesson we take a closer look at these tests, but perhaps more importantly, their strict assumptions.
8 videos, 6 readings, 2 practice quizzes
- Video: Introduction to Hypothesis Testing
- Video: Testing assumptions: Null and alternative hypothesis
- Reading: Key notes: Null and alternative hypothesis
- Video: Is there a difference?: Alternative Hypothesis
- Reading: Key notes: Alternative hypothesis
- Video: Type I and II: Hypothesis testing errors
- Reading: Key notes: Hypothesis errors
- Practice Quiz: Testing your knowledge: Hypothesis
- Video: Introduction to confidence intervals
- Reading: Key notes: Introduction to confidence intervals
- Video: How confident are you?: Confidence levels
- Reading: Key notes: Confidence levels
- Video: Interval estimation: Confidence intervals
- Reading: Key notes: Confidence intervals
- Video: (Optional) Calculating confidence intervals using spreadsheet software
- Practice Quiz: Test your knowledge: Confidence intervals
Graded: Week 4 Peer review
WEEK 5
Which test should you use?
The most common statistical test that you might come across in the literature is the t-test. There are, in actual fact, a few t-tests, but the one most are familiar with, is of course, Student’s t-test and its ubiquitous p-value. Not everyone, though, knows that the name Student was actually a pseudonym, used by William Gosset (1876 - 1937). Parametric tests have very strict assumptions that must be met before their use is justified. In this lesson we take a closer look at these tests, but perhaps more importantly, their strict assumptions. Once you know these, you will be able to identify when these tests are used inappropriately.
15 videos, 6 readings, 2 practice quizzes
- Video: Introduction to parametric tests
- Reading: Key notes: Parametric tests
- Video: Student's t-test
- Reading: Key notes: Student's t-test
- Video: ANOVA
- Reading: Key notes: ANOVA
- Video: Linear Regression
- Reading: Key notes: Linear regression
- Video: (Optional) Student's t-test in action
- Practice Quiz: Test your knowledge: Parametric tests
- Video: Introduction to nonparametric tests
- Video: Checking for normality
- Reading: Key notes: Nonparametric tests
- Video: Thinking nonparametrically
- Video: Comparing paired observations: Signs
- Video: Ordering values: Ranking
- Video: Paired comparisons: Sign ranks
- Video: Summation of ranks: Rank sums
- Video: Comparing two populations: Mann-Whitney-U test
- Video: More nonparametric tests
- Reading: Key notes: Nonparametric tests
- Practice Quiz: Test your knowledge: Non-parametric tests
- Video: Case study 3
Graded: Week 5 Graded Quiz
WEEK 6
Categorical data and analyzing accuracy of results
Congratulations! You've reached the final week of the course Understanding Clinical Research. In this lesson we will take a look at how good tests are at picking up the presence or absence of disease, helping us choose appropriate tests, and how to interpret positive and negative results. We’ll decipher sensitivity, specificity, positive and negative predictive values. You'll end of this course with a final exam, to test the knowledge and application you've learned in this course. I hope you've enjoyed this course and it helps your understanding of clinical research.
8 videos, 4 readings, 2 practice quizzes
- Video: Introduction to comparing categorical data
- Video: Observed frequencies: Contingency tables
- Video: Comparing observed and expected values: Chi-square test
- Video: Association between two variables: Fisher's exact test
- Reading: Key notes: Comparing categorical data
- Video: (Optional) Calculating chi-square test using spreadsheet software
- Practice Quiz: Testing your knowledge: Comparing categorical data
- Video: Introduction to sensitivity and specificity
- Video: Measuring performance: Sensitivity and specificity
- Video: Proportions of results: Positive and negative predictive values
- Reading: Keynotes: Sensitivity, specificity, positive and negative predictive values
- Practice Quiz: Test your knowledge: Sensitivity, specificity and predictive values
- Reading: Interesting online videos
- Reading: Congratulations on completing the course
Graded: Week 6 Final examination
FAQs
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Creators
University of Cape Town
The University of Cape Town is the oldest university in South Africa and is one of the leading research universities on the African continent. UCT has over 25 000 students, of whom 30% are postgraduate students. We offer degrees in six faculties: Commerce, Engineering & the Built Environment, Health Sciences, Humanities, Law, and Science. We pride ourself on our diverse student body, which reflects the many cultures and backgrounds of the region. We welcome international students and are currently home to thousands of international students from over 100 countries. UCT has a tradition of academic excellence that is respected world-wide and is privileged to have more than 30 A-rated researchers on our staff, all of whom are recognised as world leaders in their field. Our aim is to ensure that our research contributes to the public good through sharing knowledge for the benefit of society. Past students include five Nobel Laureates – Max Theiler, Alan Cormack, Sir Aaron Klug, Ralph Bunche and, most recently, J M Coetzee.
Ratings and Reviews
Brilliant. Should be compulsory for all registrars.
Great course content. Well elaborated and easy to understand even for non-science oriented students. It is an excellent starter for students wishing to pursue clinical research studies as it presents the important first steps for research. Thanks to you Dr Juan Klopper, the University of Cape Town, and the entire Coursera team.
Very well discussed and thorough.
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AA
A great , informative course easy to understand