Understanding Clinical Research: Behind the Statistics Course Reviews

The course is designed to equip learners with a solid understanding of the methodologies and techniques used in clinical research, enabling them to critically evaluate and interpret clinical studies.

The course covers various aspects, including the design and planning of clinical trials, ethical considerations, data collection and management, and the role of statistical analysis in drawing meaningful conclusions from research findings. Learners delve into the intricacies of study designs, such as randomized controlled trials, observational studies, and systematic reviews, learning about their strengths, limitations, and appropriate applications.

The course also emphasizes the importance of statistical literacy for healthcare professionals and researchers. Participants learn about key statistical concepts, such as hypothesis testing, p-values, confidence intervals, and effect sizes. Through practical examples and case studies, learners gain insights into how to interpret statistical results and assess the clinical relevance of research outcomes.

Moreover, the course explores challenges related to bias, confounding variables, and sources of error that can impact research outcomes. Learners are encouraged to develop a critical mindset when evaluating research methodologies and results, fostering a deeper understanding of the nuances involved in clinical research interpretation.

By the end of the course, participants should have a solid foundation in understanding clinical research methodologies, statistical analysis, and the factors influencing the validity and reliability of research findings. This knowledge equips them with the skills to contribute meaningfully to evidence-based decision-making in the field of healthcare and clinical research.

Course Content:

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.

The course Understanding Clinical Research: Behind the Statistics by Juan H. Klopper on Coursera is divided into 6 modules. Here is a detailed breakdown of the modules:

Module 1: 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.

• Introduction to Understanding Clinical Research•2 minutes•Preview module
• About the course•2 minutes
• Observing and intervening: Observational & experimental studies•3 minutes
• Observing and describing: Case series studies•3 minutes
• Comparing groups: Case-control studies•3 minutes
• Collecting data at one point in time: Cross-sectional studies•3 minutes
• Studying a group with common traits: Cohort studies•4 minutes
• Let's intervene: Experimental studies•6 minutes
• Working with existing research: Meta-analysis and Systematic Review•4 minutes
• Doing a literature search: Part 1•4 minutes
• Doing a literature search: Part 2•6 minutes

• How this course works•5 minutes
• Pre-course survey•5 minutes
• Study types•10 minutes
• Key notes: Observational and experimental studies•10 minutes
• Key notes: Case series studies•10 minutes
• Key notes: Case-control studies•10 minutes
• Key notes: Cross-sectional studies•10 minutes
• Key notes: Cohort studies•10 minutes
• Key notes: Experimental studies•10 minutes
• Key notes: Meta-analysis and systematic review•10 minutes
• Peer review introduction•10 minutes

• Test your knowledge: Study types•30 minutes

Module 2: 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.

• Introduction•2 minutes•Preview module
• Some key concepts: Definitions•4 minutes
• Data types•1 minute
• Arbitary classification: Nominal categorical data•1 minute
• Natural ordering of attributes: Ordinal categorical data•2 minutes
• Measurements and numbers: Numerical data types•3 minutes
• How to tell the difference: Discrete and continuous variables•3 minutes
• Introduction•1 minute
• Measures of central tendency•5 minutes
• Measures of dispersion•6 minutes
• (Optional) Setting up spreadsheets to do your own analysis•3 minutes
• (Optional) Descriptive statistics using spreadsheets•9 minutes
• Making inferences: Sampling•5 minutes
• Types of sampling•3 minutes
• Case study 1•9 minutes

• Key notes: Definitions•10 minutes
• Key notes: Data types•10 minutes
• Key notes: Nominal categorical data•10 minutes
• Key notes: Ordinal categorical data•10 minutes
• Key notes: Numerical data types•10 minutes
• Key notes: Discrete and continuous variables•10 minutes
• Key notes: Describing the data•10 minutes
• Key notes: Measures of central tendency•10 minutes
• Key notes: Measures of dispersion•10 minutes
• Visual representation of data•10 minutes
• Key notes: Sampling•10 minutes
• Key notes: Types of sampling•10 minutes

• Week 2 Graded Quiz•30 minutes
• Test your knowledge: Data types•30 minutes
• Test your knowledge: Measures of central tendency and dispersion•30 minutes
• Test your knowledge: Sampling•30 minutes

Module 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.

• P-values: P is for probability•1 minute•Preview module
• Working out the probability: Rolling dice•5 minutes
• Area under the curve: Continuous data types•4 minutes
• Introduction to the central limit theorem: The heart of probability theory•1 minute
• Asymmetry and peakedness: Skewness and Kurtosis•4 minutes
• Learning from the lotto: Combinations•4 minutes
• Approximating a bell-shaped curve: The central limit theorem•4 minutes
• Patterns in the data: Distributions•2 minutes
• The bell-shaped curve: Normal distribution•3 minutes
• Plotting a sample statistic: Sampling distribution•7 minutes
• Standard normal distribution: Z distribution•9 minutes
• Estimating population parameters: t-distribution•3 minutes
• (Optional) Generating random data point values using spreadsheet software•6 minutes
• Case study 2•17 minutes

• Key notes: P-values•10 minutes
• Key notes: Rolling dice•10 minutes
• Key notes: Continuous data types•10 minutes
• Introduction to the central limit theorem•10 minutes
• Key notes: Skewness and kurtosis•10 minutes
• Key notes: Combinations•10 minutes
• Key notes: Central limit theorem•10 minutes
• Key notes: Distributions•10 minutes
• Key notes: Normal distribution•10 minutes
• Key notes: Sampling distribution•10 minutes
• Key notes: Z-distribution•10 minutes
• Key notes: The t-distibution•10 minutes

• Week 3 Graded Quiz•30 minutes
• Test your knowledge: Probability•30 minutes
• Test your knowledge: The central limit theorem•30 minutes
• Test your knowledge: Distributions•30 minutes

Module 4: 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.

• Introduction to Hypothesis Testing•1 minute•Preview module
• Testing assumptions: Null and alternative hypothesis•3 minutes
• Is there a difference?: Alternative Hypothesis•4 minutes
• Type I and II: Hypothesis testing errors•3 minutes
• Introduction to confidence intervals•3 minutes
• How confident are you?: Confidence levels•3 minutes
• Interval estimation: Confidence intervals•3 minutes
• (Optional) Calculating confidence intervals using spreadsheet software•10 minutes

• Key notes: Null and alternative hypothesis•10 minutes
• Key notes: Alternative hypothesis•10 minutes
• Key notes: Hypothesis errors•10 minutes
• Key notes: Introduction to confidence intervals•10 minutes
• Key notes: Confidence levels•10 minutes
• Key notes: Confidence intervals•10 minutes

• Testing your knowledge: Hypothesis•30 minutes
• Test your knowledge: Confidence intervals•30 minutes

Module 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.

• Introduction to parametric tests•2 minutes•Preview module
• Student's t-test•15 minutes
• ANOVA•4 minutes
• Linear Regression•4 minutes
• (Optional) Student's t-test in action•12 minutes
• Introduction to nonparametric tests•3 minutes
• Checking for normality•5 minutes
• Thinking nonparametrically•2 minutes
• Comparing paired observations: Signs•2 minutes
• Ordering values: Ranking•2 minutes
• Paired comparisons: Sign ranks•2 minutes
• Summation of ranks: Rank sums•6 minutes
• Comparing two populations: Mann-Whitney-U test•4 minutes
• More nonparametric tests•5 minutes
• Case study 3•13 minutes

• Key notes: Parametric tests•10 minutes
• Key notes: Student's t-test•10 minutes
• Key notes: ANOVA•10 minutes
• Key notes: Linear regression•10 minutes
• Key notes: Nonparametric tests•10 minutes
• Key notes: Nonparametric tests•10 minutes

• Week 5 Graded Quiz•30 minutes
• Test your knowledge: Parametric tests•30 minutes
• Test your knowledge: Non-parametric tests•30 minutes

Module 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.

• Introduction to comparing categorical data•2 minutes•Preview module
• Observed frequencies: Contingency tables•5 minutes
• Comparing observed and expected values: Chi-square test•3 minutes
• Association between two variables: Fisher's exact test•2 minutes
• (Optional) Calculating chi-square test using spreadsheet software•7 minutes
• Introduction to sensitivity and specificity•2 minutes
• Measuring performance: Sensitivity and specificity•4 minutes
• Proportions of results: Positive and negative predictive values•6 minutes
• Introdution to risk and odds ratios•0 minutes
• Risk and odds ratios - Losses (Risk)•6 minutes
• Risk and odds ratios - Losses (Odds)•6 minutes
• Risk and odds ratios - Wins•4 minutes
• Risk and odds ratios example•4 minutes

• Key notes: Comparing categorical data•10 minutes
• Keynotes: Sensitivity, specificity, positive and negative predictive values•10 minutes
• Congratulations on completing the course•10 minutes
• Risk and odds ratios•10 minutes

• Week 6 Final examination•30 minutes
• Honors: Risk and odds ratios•0 minutes
• Testing your knowledge: Comparing categorical data•30 minutes
• Test your knowledge: Sensitivity, specificity and predictive values•30 minutes

Reviews:

As a former participant of the Understanding Clinical Research: Behind the Statistics course by Juan H. Klopper on Coursera, I am pleased to provide an evaluation of the course based on my experience.

This course proved to be an enlightening journey into the realm of clinical research and its intricate connection to statistical analysis. Dr. Klopper's expertise in the field became evident from the very start. His clear and concise explanations effectively demystified complex concepts, making them accessible to learners with varying levels of prior knowledge.

One of the standout features of the course was its well-structured curriculum. The progression from fundamental concepts to more advanced topics was seamless, allowing participants to build a strong foundation before delving into more nuanced areas. The inclusion of real-world case studies and practical examples brought a tangible dimension to the theoretical content, making it easier to grasp the practical implications of statistical findings.

The emphasis on critical evaluation was a hallmark of the course. Through assignments and discussions, I honed my ability to dissect research methodologies, identify potential biases, and assess the validity of statistical results. This skill has proven invaluable not only in my understanding of research papers but also in my day-to-day interactions with medical literature.

Dr. Klopper's teaching style fostered an engaging and interactive learning environment. His passion for the subject matter shone through, keeping me consistently motivated to explore each module. Moreover, the sense of community within the online platform, facilitated by active discussions and peer interactions, added a collaborative dimension to the learning experience.

The course's practical relevance cannot be understated. The knowledge gained has empowered me to critically evaluate medical studies, understand the nuances of statistical significance, and engage in evidence-based decision-making. The course's application in real-world medical scenarios has been evident, and I find myself better equipped to contribute meaningfully to discussions with colleagues and healthcare professionals.

In conclusion, Understanding Clinical Research: Behind the Statistics is a standout course for anyone seeking to bridge the gap between clinical research and statistical analysis. Dr. Klopper's expertise, the comprehensive curriculum, and the emphasis on critical thinking make it a transformative educational experience. The skills acquired extend beyond the course itself, impacting one's ability to navigate the dynamic landscape of medical research with confidence and insight.

What you'll learn:

Upon completing the course Understanding Clinical Research: Behind the Statistics by Juan H. Klopper on Coursera, participants will acquire a range of valuable skills:

1. Critical Evaluation: Learners will develop the ability to critically evaluate clinical research studies, assessing the validity of study designs, methodologies, and statistical analyses. This skill is essential for making informed decisions about the applicability and reliability of research findings.

2. Statistical Literacy: Participants will gain a solid understanding of fundamental statistical concepts, such as p-values, confidence intervals, effect sizes, and hypothesis testing. This statistical literacy empowers them to interpret and communicate the significance of research results accurately.

3. Study Design Knowledge: Learners will become familiar with various types of study designs, including randomized controlled trials, observational studies, and systematic reviews. This knowledge enables them to understand the strengths and limitations of different approaches to clinical research.

4. Data Interpretation: Through practical examples and case studies, participants will learn how to interpret statistical outcomes in a clinical context. This skill enables them to draw meaningful conclusions from research data and consider the clinical implications of findings.

5. Ethical Awareness: The course covers ethical considerations in clinical research, helping learners understand the importance of ethical guidelines and regulations in conducting research involving human subjects.

6. Research Collaboration: Participants will be better equipped to collaborate effectively with researchers and healthcare professionals, as they will share a common understanding of research methodologies and statistical concepts.

7. Evidence-Based Decision Making: Armed with a deeper understanding of clinical research, learners can contribute more effectively to evidence-based decision-making in healthcare settings, ensuring that decisions are grounded in sound research findings.

8. Bias and Confounding Recognition: The course highlights challenges related to bias, confounding variables, and sources of error in research. Participants will develop the ability to recognize these issues and consider their impact on research results.

9. Communication Skills: Learners will enhance their ability to communicate research findings and their implications to both technical and non-technical audiences, bridging the gap between researchers, healthcare professionals, and the public.

10. Continuous Learning: The course fosters a mindset of continuous learning in the field of clinical research, encouraging participants to stay updated with new methodologies, advancements, and best practices.

Overall, completing this course equips participants with the knowledge and skills needed to engage with clinical research critically, contribute to evidence-based healthcare practices, and make informed decisions based on a solid understanding of statistical analysis and research methodologies.

Author:

Dr. Juan H. Klopper is a prominent figure in the field of clinical research and medicine. With a distinguished career spanning both academia and clinical practice, he has made significant contributions to the understanding and advancement of healthcare through research and education.

Dr. Klopper is a seasoned medical professional who has demonstrated expertise in multiple areas of clinical medicine, research methodology, and medical education. He has a background in endocrinology and thyroid disorders, with a specific focus on thyroid cancer. His extensive experience in patient care, coupled with his dedication to research, has allowed him to bridge the gap between theoretical knowledge and practical application in the medical field.

As an educator, Dr. Klopper is known for his ability to convey complex medical concepts in an accessible and engaging manner. He has been involved in various educational initiatives, including online courses like "Understanding Clinical Research: Behind the Statistics" on Coursera. Through his teaching, he empowers learners to understand the intricacies of clinical research methodologies, statistical analysis, and critical evaluation of research findings.

Dr. Klopper's scholarly contributions include research papers, articles, and presentations on topics ranging from clinical trial design to thyroid cancer management. His work reflects a dedication to evidence-based medicine and a commitment to improving patient outcomes through rigorous research and informed decision-making.

Given his background, experience, and commitment to advancing healthcare knowledge, Dr. Juan H. Klopper is widely regarded as a reputable expert in the field of clinical research and medical education. His multidisciplinary expertise, ability to simplify complex concepts, and dedication to enhancing research literacy make him a valuable asset to both the medical community and individuals seeking to deepen their understanding of clinical research and statistics.

Requirements:

The course Understanding Clinical Research: Behind the Statistics authored by Juan H. Klopper, typically includes the following requirements:

1. Interest in Clinical Research: Participants should have an interest in clinical research methodologies, statistical analysis, and their application in healthcare and medical decision-making.

2. Basic Medical Knowledge: While the course does not usually require an extensive medical background, a basic understanding of medical terminology and concepts can be beneficial for comprehending the content.

3. Access to Online Learning Platform: As the course is often hosted on platforms like Coursera, participants need access to a computer, tablet, or smartphone with an internet connection to participate in lectures, discussions, and assignments.

4. English Proficiency: Since the course materials and instruction are in English, a reasonable level of proficiency in English reading, writing, and listening is important.

5. Commitment to Learning: Like any educational endeavor, the course demands a commitment of time and effort. Participants should be prepared to engage with course materials, complete assignments, and actively participate in discussions.

6. Open-Mindedness: The course covers a range of topics related to clinical research, statistical analysis, and medical decision-making. An open-minded approach to learning and a willingness to explore new concepts are beneficial.

7. Critical Thinking Skills: The course encourages critical evaluation of research methodologies and statistical results. Participants should possess or be willing to develop critical thinking skills to assess the validity and significance of research findings.

8. Computer Literacy: Basic computer skills, such as navigating online platforms, participating in online discussions, and submitting assignments, are essential for engaging with the course content.

9. Time Management: The course may have a structured schedule with assignments and deadlines. Participants should manage their time effectively to complete the course requirements within the specified time frame.

10. Desire to Apply Learning: The course is designed to equip learners with practical skills to interpret clinical research and statistical results. A willingness to apply the knowledge gained to real-world scenarios in healthcare is valuable.

These requirements collectively contribute to a meaningful and engaging learning experience in the Understanding Clinical Research: Behind the Statistics course, enabling participants to enhance their understanding of clinical research methodologies and their role in medical decision-making.