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University of Cape Town

Understanding Clinical Research: Behind the Statistics

University of Cape Town via Coursera

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Overview

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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.

Syllabus

  • Getting things started by defining study types
    • Welcome to the first week. Here we’ll provide an intuitive understanding of clinical research results. So this isn’t a comprehensive statistics course - rather it offers a practical orientation to the field of medical research and commonly used statistical analysis. The first topics we will look at are research methods and data collection with a specific focus on study types. By the end, you should be able to identify which study types are being used and why the researchers selected them, when you are later reading a published paper.
  • Describing your data
    • 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. This week I am going to tackle the differences in data that determine what type of statistical test we can use in making sense of our data.
  • 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.
  • The important first steps: Hypothesis testing and confidence levels
    • In general, a researcher has a question in mind that he or she needs to answer. Everyone might have an opinion on this question (or answer), but a researcher looks for the answer by designing an experiment and investigating the outcome. First, 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.
  • 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.
  • 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.

Taught by

Dr Juan H Klopper

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Reviews

4.9 rating, based on 889 Class Central reviews

4.8 rating at Coursera based on 3676 ratings

Start your review of Understanding Clinical Research: Behind the Statistics

  • Profile image for Pardha Sai Kumar
    Pardha Sai Kumar @saikumar
    29
    Overall good, but the course lacks practical examples like demos. E.g how to create dummy data for t-distribution using spread sheet software. Require more examples on nonparametric tests. I feel nonparametric tests are not explained properly. For example, rank sum doesn't make complete sense The course does not explain shortcomings of p value in larger samples. Lastly, there is no explanation on logistic regression that would have made this course complete. This course is nice overview for someone who wants to have basic understanding of clinical research.
  • Bhavna Krishnan
    19
    What a great course! I highly recommend it to anyone who is interested in clinical research and wants to understand how statistics is used in clinical research. I loved all aspects of the course. The lecture videos were short and crisp. Dr. Klopper is very engaging and explained even the hardest concepts really well. The quizzes let you apply what you learn. The peer review assignments are a great way of soliciting and giving feedback. Learning this course has really enriched my statistics knowledge.
  • Anonymous
    As a surgeon I had some knowledge about statistics before undergoing the course. But really not very much. For a dummy, facing a statistics course might be challenging. But not this particular course. And basically for three important reasons: 1. Th…
  • Profile image for Arnab Dasgupta
    Arnab Dasgupta
    7
    I have attempted taking courses or reading books on medical statistics earlier, and every time, I took a few baby steps and then aborted. I was good at maths in school, but hey, twenty years in the medical profession, and the confidence sags. This time, I got a bird's eye view of the entire subject, with sufficient detail where required. This course is comprehensive, without being intimidating, and focuses on an intuitive grasp of the subject. I can say for sure that I am more motivated now than ever before, in conducting clinical research the right way. The foundation stones have been laid. I can now build on this knowledge, without fear of statistics getting in the way.
  • Profile image for Najmee Adulyarat
    Najmee Adulyarat @17jjy
    2
    good way to start with clinical statistic life. The course could help your life easier and change your attitude about research.
  • Anonymous
    I found this course good for beginners. Although I have only high school education I didn't find it hard.
    I learned a lot of new terms and understood the basics of some interesting statistical concepts.

    The lessons were very clear, the use of graphs helped me a great deal, Teaching free data retrieval and analysis tools was cool, and the Keynotes were great for review.
    The quizzes and tasks are fair, and easier then i expected.

    D.R. Klopper Thanks a lot,
    You were very pleasant and you seem to have sincere interest in the subject and teaching it.
    I promise that if i publish a research, I won't forget to enable free accesses to the data!
  • Anonymous
    The best course of statistics in healthcare research. The lecturer uses simple language, repeatedly and clearly explains the concepts with practical examples. The videos are short, yet informative which helps you save time and keep interest in the course. The practice questions and graded assessments reflects the knowledge in videos and notes. Most of the pertinent topics biostatistics of healthcare research were covered in this course.
    Thanks Juan and team,

    Jean de Dieu
  • Anonymous
    Incredible course. I´m a medical doctor recently graduated. This course has given me a different perspective of the clinical research. It showed me that I had dragged many errors and non-solid concepts. By doing this course, I got over those empty spaces. Thank you. I highly recommend it to everyone!
  • Anonymous
    The training was very well constructed and clear. The English used was very clear, and the information was well organized and progressively structured, which greatly facilitated understanding. It was illustrated with concrete, simple examples well adapted to the target audience. Thank you, Dr Klopper, for all of this.
  • Anonymous
    The course is very well structured. The theory is clearly explained with appropriate examples, tempo and progression to new topics. The combination between theory, tests and practical exercises helps very well to anchor the learned contents and makes the course really fun. Thank you for the great course!
  • Anonymous
    "Understanding Clinical Research: Behind the Statistics" is an exceptional resource for medical professionals and students who find traditional biostatistics intimidating. Its primary strength lies in its ability to bridge the gap between abstract mathematical formulas and practical clinical application. Overall, it is a highly recommended course for anyone looking to critically appraise research papers with confidence and integrate evidence-based medicine into their practice more effectively.
  • Anonymous
    Dr Klopper is an outstanding teacher. The course was very well structured, and easy to follow and understand, especially since the examples being used were that of published research studies. The graded assignments in particular were ingenious and fun to complete.
    Dr Klopper's cadence too was commendable as never once did it fail to keep attention.
    Thank you so much to Dr Klopper and team.
  • Profile image for Shehryar Chaudhry
    Shehryar Chaudhry
    I found Understanding Clinical Research: Behind the Statistics to be an excellent introduction to the principles of clinical research and statistical analysis. The course effectively bridges the gap between theoretical statistics and practical appli…
  • Anonymous
    It's a wonderfully designed course with an extremely helpful set of notes. The team has done a great job of condensing important information in a concise yet precise manner.
  • Anonymous
    I found this course very much productive and helpful in understanding in depth learning this course different topics were highly appreciated good precise given examples very worthy.
    This beigner level course enable student to, learn basics of clinical trial with statistics.
    At some occasionally I feel something going beyond my head don't know why, but I'm sure these things will be covered with more examples.
    The cousera platform was very helpful in a sanse that daily based schedules activities and assignments stuuning performance , it really enthuse the candidates to remain with the course.
    I'm with deep heartly thankful all the team.
  • Anonymous
    I found this course very much productive and helpful in understanding in depth learning this course different topics were highly appreciated good precise given examples very worthy.
    This beigner level course enable student to, learn basics of clinical trial with statistics.
    At some occasionally I feel something going beyond my head don't know why, but I'm sure these things will be covered with more examples.
    The cousera platform was very helpful in a sanse that daily based schedules activities and assignments stuuning performance , it really enthuse the candidates to remain with the course.
    I'm with deep heartly thankful all the team.
  • Anonymous
    Thank you so much sir!!!! this course has helped me a lot in understanding biostats in clinical research in a very simple way. loved your classes, forever grateful, namaste.
  • Saurabh Kala
    4
    Definitely one of the better courses on the subject. Highly recommended. However, a basic understanding of statistics might be required to understand the matter.
  • Anonymous
    It's a good course for those who want to brush up on statistics. Classes and notes are crisp. Even for people with a hectic schedule these classes are doable as they are for shorter duration. Thank you for this course.
  • Anonymous
    I found this course very in depth helpful in understanding statistics in clinical trial, daily task based learning give alot of enthusiasm for learning and eager for it. I found this course very productive the platform provided by Coursera is very worthy and student friendly, I hope and whishes it may gi furthur and polished, well there were something which I found bit difficult but im sure with time all such will be improved and come with more furnished.
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