À propos de ce cours : 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.

Créé par : University of Cape Town

Enseigné par : Juan H Klopper, Dr
Department of Surgery

Engagement	6 weeks of study, 2-3 hours/week
Langue	English
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.7 étoiles Note moyenne des utilisateurs 4.7Voir ce que disent les étudiants

Programme de cours

SEMAINE 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 vidéos, 11 lectures, 1 quiz pour s'exercer

Vidéo: About the course
Leyendo: How this course works
Leyendo: Pre-course survey
Cuadro de aviso de la discusión: Introduce yourself to your peers
Leyendo: Study types
Vidéo: Observing and intervening: Observational & experimental studies
Leyendo: Key notes: Observational and experimental studies
Vidéo: Observing and describing: Case series studies
Leyendo: Key notes: Case series studies
Vidéo: Comparing groups: Case-control studies
Leyendo: Key notes: Case-control studies
Vidéo: Collecting data at one point in time: Cross-sectional studies
Leyendo: Key notes: Cross-sectional studies
Vidéo: Studying a group with common traits: Cohort studies
Leyendo: Key notes: Cohort studies
Vidéo: Let's intervene: Experimental studies
Leyendo: Key notes: Experimental studies
Vidéo: Working with existing research: Meta-analysis and Systematic Review
Leyendo: Key notes: Meta-analysis and systematic review
Cuestionario de práctica: Test your knowledge: Study types
Leyendo: Peer review introduction
Vidéo: Doing a literature search: Part 1
Vidéo: Doing a literature search: Part 2

Noté: Week 1: Navigating Clinical Research

SEMAINE 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 vidéos, 12 lectures, 3 quiz pour s'exercer

Vidéo: Some key concepts: Definitions
Leyendo: Key notes: Definitions
Vidéo: Data types
Leyendo: Key notes: Data types
Vidéo: Arbitary classification: Nominal categorical data
Leyendo: Key notes: Nominal categorical data
Vidéo: Natural ordering of attributes: Ordinal categorical data
Leyendo: Key notes: Ordinal categorical data
Vidéo: Measurements and numbers: Numerical data types
Leyendo: Key notes: Numerical data types
Vidéo: How to tell the difference: Discrete and continuous variables
Leyendo: Key notes: Discrete and continuous variables
Cuestionario de práctica: Test your knowledge: Data types
Vidéo: Introduction
Leyendo: Key notes: Describing the data
Vidéo: Measures of central tendency
Leyendo: Key notes: Measures of central tendency
Vidéo: Measures of dispersion
Leyendo: Key notes: Measures of dispersion
Leyendo: Visual representation of data
Vidéo: (Optional) Setting up spreadsheet software to do your own analysis
Vidéo: (Optional) Descriptive statistics using spreadsheet software
Cuestionario de práctica: Test your knowledge: Measures of central tendency and dispersion
Vidéo: Making inferences: Sampling
Leyendo: Key notes: Sampling
Vidéo: Types of sampling
Leyendo: Key notes: Types of sampling
Cuestionario de práctica: Test your knowledge: Sampling
Vidéo: Case study 1
Cuadro de aviso de la discusión: Share an example of clinical research

Noté: Week 2 Graded Quiz

SEMAINE 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 vidéos, 12 lectures, 3 quiz pour s'exercer

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

Noté: Week 3 Graded Quiz

SEMAINE 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 vidéos, 6 lectures, 2 quiz pour s'exercer

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

Noté: Week 4 Peer review

SEMAINE 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 vidéos, 6 lectures, 2 quiz pour s'exercer

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

Noté: Week 5 Graded Quiz

SEMAINE 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 vidéos, 4 lectures, 2 quiz pour s'exercer

Vidéo: Observed frequencies: Contingency tables
Vidéo: Comparing observed and expected values: Chi-square test
Vidéo: Association between two variables: Fisher's exact test
Leyendo: Key notes: Comparing categorical data
Vidéo: (Optional) Calculating chi-square test using spreadsheet software
Cuestionario de práctica: Testing your knowledge: Comparing categorical data
Vidéo: Introduction to sensitivity and specificity
Vidéo: Measuring performance: Sensitivity and specificity
Vidéo: Proportions of results: Positive and negative predictive values
Leyendo: Keynotes: Sensitivity, specificity, positive and negative predictive values
Cuestionario de práctica: Test your knowledge: Sensitivity, specificity and predictive values
Leyendo: Interesting online videos
Leyendo: Congratulations on completing the course

Noté: Week 6 Final examination

FAQ

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Comment cela fonctionne

Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.

Certificados

Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

Créateurs

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.

Notation et examens

Note moyenne 4.7 sur 5 sur 728 notes

This course gives general informations about the medical statistics.

It's easy to follow and I found it very useful

i found this course very intuitive in leveraging the basic understanding of statistics and why a particular statistic is applied in clinical. The course is unique providing practical exposure in understanding the topics like fetching article in pubmed, p-value, need of central limit theorem, hypothesis testing, ANOVA, sensitivity and specificity analysis used by the authors. I am pretty sure i applied this course practically to my student in class and results are encouraging.

After this course, you'll understand the basic knowledge, common statistic methods and which one to use for different data types. Note that the detailed math calculations behind the methods are NOT the aim for this course.