Informações sobre o curso: 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.

Desenvolvido por: University of Cape Town

Ministrado por: Juan H Klopper, Dr
Department of Surgery

Compromisso	6 semanas de estudo, 2-3 horas/semana
Idioma	English
Como ser aprovado	Seja aprovado em todas as tarefas para concluir o curso.
Classificação do usuário	4.7 estrelas Classificação média do usuário 4.7Veja o que os aprendizes disseram

Programa

SEMANA 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 leituras, 1 teste para praticar

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

Nota atribuída: Week 1: Navigating Clinical Research

SEMANA 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 leituras, 3 testes para praticar

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

Nota atribuída: Week 2 Graded Quiz

SEMANA 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 leituras, 3 testes para praticar

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

Nota atribuída: Week 3 Graded Quiz

SEMANA 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 leituras, 2 testes para praticar

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

Nota atribuída: Week 4 Peer review

SEMANA 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 leituras, 2 testes para praticar

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

Nota atribuída: Week 5 Graded Quiz

SEMANA 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 leituras, 2 testes para praticar

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

Nota atribuída: Week 6 Final examination

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Kursarbeit

Jeder Kurs ist mit einem interaktiven Sachbuch vergleichbar und beinhaltet im Voraus aufgenommene Videos, Quiz und Projekte.

Unterstützung durch die anderen Kursteilnehmer

Nehmen Sie Kontakt mit mehreren tausend anderen Kursteilnehmern auf, erörtern Sie Ideen, diskutieren Sie Kursmaterialien und erhalten Sie Hilfe bei der Bewerkstelligung von Konzepten.

Zertifikate

Lassen Sie sich eine offizielle Anerkennung Ihrer Arbeit ausstellen und teilen Sie Ihre Erfolge mit Freunden, Kollegen und Arbeitgebern.

Desenvolvedores

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.

Classificações e avaliações

Avaliado em 4.7 de 5 decorrente de 729 avaliações

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.