Getting things started by defining study types-Welcome to the course
Introduction to Understanding Clinical Research
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About the course
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How this course works
Pre-course survey
Getting things started by defining study types-What are the different clinical study types?
Study types
Observing and intervening: Observational & experimental studies
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Key notes: Observational and experimental studies
Observing and describing: Case series studies
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Key notes: Case series studies
Comparing groups: Case-control studies
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Key notes: Case-control studies
Collecting data at one point in time: Cross-sectional studies
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Key notes: Cross-sectional studies
Studying a group with common traits: Cohort studies
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Key notes: Cohort studies
Let's intervene: Experimental studies
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Key notes: Experimental studies
Working with existing research: Meta-analysis and Systematic Review
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Key notes: Meta-analysis and systematic review
Getting things started by defining study types-Navigating clinical research: Peer review exercise
Peer review introduction
Doing a literature search: Part 1
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Doing a literature search: Part 2
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Describing your data -The spectrum of data types
Introduction
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Some key concepts: Definitions
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Key notes: Definitions
Data types
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Key notes: Data types
Arbitary classification: Nominal categorical data
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Key notes: Nominal categorical data
Natural ordering of attributes: Ordinal categorical data
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Key notes: Ordinal categorical data
Measurements and numbers: Numerical data types
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Key notes: Numerical data types
How to tell the difference: Discrete and continuous variables
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Key notes: Discrete and continuous variables
Describing your data -Summarising data through simple descriptive statistics
Introduction
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Key notes: Describing the data
Measures of central tendency
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Key notes: Measures of central tendency
Measures of dispersion
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Key notes: Measures of dispersion
Visual representation of data
(Optional) Setting up spreadsheets to do your own analysis
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(Optional) Descriptive statistics using spreadsheets
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Describing your data -Methods of obtaining sample subjects for research
Making inferences: Sampling
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Key notes: Sampling
Types of sampling
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Key notes: Types of sampling
Describing your data -End of Section 2
Case study 1
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Building an intuitive understanding of statistical analysis-From area to probability
P-values: P is for probability
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Key notes: P-values
Working out the probability: Rolling dice
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Key notes: Rolling dice
Area under the curve: Continuous data types
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Key notes: Continuous data types
Building an intuitive understanding of statistical analysis- The heart of inferential statistics: Central limit theorem
Introduction to the central limit theorem: The heart of probability theory
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Introduction to the central limit theorem
Asymmetry and peakedness: Skewness and Kurtosis
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Key notes: Skewness and kurtosis
Learning from the lotto: Combinations
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Key notes: Combinations
Approximating a bell-shaped curve: The central limit theorem
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Key notes: Central limit theorem
Building an intuitive understanding of statistical analysis-Distributions: The shape of data
Patterns in the data: Distributions
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Key notes: Distributions
The bell-shaped curve: Normal distribution
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Key notes: Normal distribution
Plotting a sample statistic: Sampling distribution
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Key notes: Sampling distribution
Standard normal distribution: Z distribution
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Key notes: Z-distribution
Estimating population parameters: t-distribution
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Key notes: The t-distibution
(Optional) Generating random data point values using spreadsheet software
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Case study 2
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The important first steps: Hypothesis testing and confidence levels-Reject or fail to reject? That is hypothesis testing.
Introduction to Hypothesis Testing
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Testing assumptions: Null and alternative hypothesis
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Key notes: Null and alternative hypothesis
Is there a difference?: Alternative Hypothesis
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Key notes: Alternative hypothesis
Type I and II: Hypothesis testing errors
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Key notes: Hypothesis errors
The important first steps: Hypothesis testing and confidence levels-Confidence in your results
Introduction to confidence intervals
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Key notes: Introduction to confidence intervals
How confident are you?: Confidence levels
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Key notes: Confidence levels
Interval estimation: Confidence intervals
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Key notes: Confidence intervals
(Optional) Calculating confidence intervals using spreadsheet software
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Which test should you use?-Parametric testing for your normal data
Introduction to parametric tests
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Key notes: Parametric tests
Student's t-test
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Key notes: Student's t-test
ANOVA
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Key notes: ANOVA
Linear Regression
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Key notes: Linear regression
(Optional) Student's t-test in action
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Which test should you use?-Non-parametric testing for your non-normal data
Introduction to nonparametric tests
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Checking for normality
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Key notes: Nonparametric tests
Thinking nonparametrically
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Comparing paired observations: Signs
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Ordering values: Ranking
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Paired comparisons: Sign ranks
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Summation of ranks: Rank sums
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Comparing two populations: Mann-Whitney-U test
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More nonparametric tests
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Key notes: Nonparametric tests
Case study 3
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Categorical data and analyzing accuracy of results -Comparing categorical data
Introduction to comparing categorical data
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Observed frequencies: Contingency tables
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Comparing observed and expected values: Chi-square test
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Association between two variables: Fisher's exact test
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Key notes: Comparing categorical data
(Optional) Calculating chi-square test using spreadsheet software
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Categorical data and analyzing accuracy of results -Sensitivity, specificity and predictive values
Introduction to sensitivity and specificity
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Measuring performance: Sensitivity and specificity
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Proportions of results: Positive and negative predictive values
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Keynotes: Sensitivity, specificity, positive and negative predictive values
Congratulations on completing the course
Categorical data and analyzing accuracy of results -Relative risks
Risk and odds ratios
Introdution to risk and odds ratios
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Risk and odds ratios - Losses (Risk)
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Risk and odds ratios - Losses (Odds)
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Risk and odds ratios - Wins
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Risk and odds ratios example
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