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If you've ever skipped over the results section of a medical paper because terms like "confidence interval" or "p-value" confuse you, you've come to the right place. You could be a clinical practitioner reading research articles to stay current on developments in your field, or you could be a medical student unsure how to approach your own research. Greater confidence in statistical analysis and results can benefit both working professionals and those conducting their own research.
This course is your first step if you simply want to properly understand the published literature or if you want to conduct your own research. It provides an easy way to begin interpreting common statistical concepts without getting bogged down in nitty-gritty mathematical formula.Understanding and being able to interpret these concepts is the best way to begin your journey into the world of clinical literature. This is where this course comes in, so let's get started!
Syllabus :
1. Getting things started by defining study types
- Introduction to Understanding Clinical Research
- About the course
- Observing and intervening: Observational & experimental studies
- Observing and describing: Case series studies
- Comparing groups: Case-control studies
- Collecting data at one point in time: Cross-sectional studies
- Studying a group with common traits: Cohort studies
- Let's intervene: Experimental studies
- Working with existing research: Meta-analysis and Systematic Review
- Doing a literature search: Part 1
- Doing a literature search: Part 2
2. Describing your data
- Introduction
- Some key concepts: Definitions
- Data types
- Arbitary classification: Nominal categorical data
- Natural ordering of attributes: Ordinal categorical data
- Measurements and numbers: Numerical data types
- How to tell the difference: Discrete and continuous variables
- Introduction
- Measures of central tendency
- Measures of dispersion
- (Optional) Setting up spreadsheets to do your own analysis
- (Optional) Descriptive statistics using spreadsheets
- Making inferences: Sampling
- Types of sampling
- Case study 1
3. Building an intuitive understanding of statistical analysis
- P-values: P is for probability
- Working out the probability: Rolling dice
- Area under the curve: Continuous data types
- Introduction to the central limit theorem: The heart of probability theory
- Asymmetry and peakedness: Skewness and Kurtosis
- Learning from the lotto: Combinations
- Approximating a bell-shaped curve: The central limit theorem
- Patterns in the data: Distributions
- The bell-shaped curve: Normal distribution
- Plotting a sample statistic: Sampling distribution
- Standard normal distribution: Z distribution
- Estimating population parameters: t-distribution
- (Optional) Generating random data point values using spreadsheet software
- Case study 2
4. The important first steps: Hypothesis testing and confidence levels
- Introduction to Hypothesis Testing
- Testing assumptions: Null and alternative hypothesis
- Is there a difference?: Alternative Hypothesis
- Type I and II: Hypothesis testing errors
- Introduction to confidence intervals
- How confident are you?: Confidence levels
- Interval estimation: Confidence intervals
- (Optional) Calculating confidence intervals using spreadsheet software
5. Which test should you use?
- Introduction to parametric tests
- Student's t-test
- ANOVA
- Linear Regression
- (Optional) Student's t-test in action
- Introduction to nonparametric tests
- Checking for normality
- Thinking nonparametrically
- Comparing paired observations: Signs
- Ordering values: Ranking
- Paired comparisons: Sign ranks
- Summation of ranks: Rank sums
- Comparing two populations: Mann-Whitney-U test
- More nonparametric tests
- Case study 3
6. Categorical data and analyzing accuracy of results
- Introduction to comparing categorical data
- Observed frequencies: Contingency tables
- Comparing observed and expected values: Chi-square test
- Association between two variables: Fisher's exact test
- (Optional) Calculating chi-square test using spreadsheet software
- Introduction to sensitivity and specificity
- Measuring performance: Sensitivity and specificity
- Proportions of results: Positive and negative predictive values
- Introdution to risk and odds ratios
- Risk and odds ratios - Losses (Risk)
- Risk and odds ratios - Losses (Odds)
- Risk and odds ratios - Wins
- Risk and odds ratios example
