Understanding Clinical Research: Behind the Statistics

Go to Course: https://www.coursera.org/learn/clinical-research

Introduction

**Course Review: Understanding Clinical Research: Behind the Statistics on Coursera** In the rapidly evolving field of medicine, the ability to comprehend and critically evaluate clinical research is indispensable for practitioners and students alike. The Coursera course, **Understanding Clinical Research: Behind the Statistics**, expertly fills this gap by demystifying statistical analysis and enhancing your confidence in navigating medical literature. **Overview of the Course** This course is designed for anyone who has ever felt overwhelmed by the technical jargon prevalent in research articles. Whether you are a medical professional seeking to stay current with the latest findings or a student preparing to embark on your own research projects, this course offers an accessible introduction to the statistical concepts that underpin clinical studies. **Syllabus Breakdown** The course unfolds over six weeks, each delving deeper into the essentials of clinical research and statistics: 1. **Getting Things Started by Defining Study Types**: The first week provides clarity on various research methods and the rationale behind selecting specific study types. This foundational understanding equips you with the tools to determine how a study’s design impacts its results. 2. **Describing Your Data**: The second week dives into common statistical tests—such as the t-test, Mann-Whitney-U test, and chi-squared test—highlighting their applications based on data types. This segment is particularly useful for grasping why certain analyses are employed over others. 3. **Building an Intuitive Understanding of Statistical Analysis**: In this week, the course tackles the infamous p-value, often misunderstood in the literature. You’ll learn about its limitations and complexities, alongside foundational concepts like the Central Limit Theorem and data distribution. 4. **The Important First Steps: Hypothesis Testing and Confidence Levels**: Here, you'll explore hypothesis testing, ethical considerations in research, and the often-misunderstood confidence intervals. This segment emphasizes the critical aspects of designing experiments and interpreting results accurately. 5. **Which Test Should You Use?**: The course further clarifies the varying t-tests and their specific assumptions, empowering you to recognize when these tests are misapplied—a crucial skill for any researcher. 6. **Categorical Data and Analyzing Accuracy of Results**: In the final week, the focus turns to evaluating diagnostic tests. You’ll learn to interpret sensitivity, specificity, and predictive values, culminating in a comprehensive understanding of how clinical tests should perform. **Why This Course Stands Out** One of the standout features of this course is its blend of theoretical knowledge and practical application. The instructor employs a straightforward pedagogical style, breaking down complex concepts into digestible pieces. The lessons include real-world examples that help bridge the gap between abstract statistics and practical relevance in healthcare. Additionally, each week ends with engaging quizzes and discussions that reinforce learning and encourage critical thinking. The final exam is a well-crafted review of all the topics covered, solidifying your understanding before moving forward in your professional journey. **Who Should Enroll?** I highly recommend this course to a diverse audience: - **Medical Practitioners**: You will benefit from a deeper understanding of research studies that inform clinical decisions and policies. - **Medical Students**: This course provides a solid groundwork for conducting your own research and critically evaluating existing literature. - **Healthcare Professionals**: Anyone involved in patient care or research who wishes to increase their research literacy will find immense value in this course. **Conclusion and Recommendation** In an age where clinical decisions are frequently driven by research outcomes, the ability to critically assess and understand these studies is paramount. **Understanding Clinical Research: Behind the Statistics** on Coursera provides the knowledge and confidence needed to successfully interpret medical research. I wholeheartedly recommend this course for anyone looking to enhance their understanding of clinical research. It not only prepares you to navigate the complex world of medical statistics but also arms you with the skills to make informed and evidence-based decisions in your healthcare practice. Enroll today and take your first step towards becoming more research-savvy in your clinical endeavors!

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.

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.

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.

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.

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.

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.