EXMD 634: Introduction to Quantitative Methods in Experimental Medicine
Summer 2020
1 Contents
- Course details
1.1 Lecture 1
- Precision vs. Bias
- Random Sampling and Randomization
- Introduction to R
1.2 Lecture 2
- Types of variables
- Introduction to probability
- Definition
- Probability rules
- Probability trees
- Bayes Theorem
- Two common probability distributions
- Binomial distribution
- Normal distribution
- Other probability distributions for categorical and continuous variables
1.3 Lecture 3
- Mean and standard deviation
- Central Limit Theorem
- Confidence intervals
- Inference for a single mean or difference in means
- Sample size calculation for a single mean or difference in means
1.4 Lecture 4
- Hypothesis testing
- Inference for a single mean or difference in means
- Sample size calculation for a single mean or difference in means
- Confidence intervals vs. hypothesis testing: Quantifying uncertainty vs. making decisions
- Extra Problems
1.5 Lecture 5
- Sample Size Calculation
1.6 Lecture 6
- Bayesian inference for a single mean and for the difference between means
- Hypothesis testing and the risk of wrong conclusions
1.7 Lecture 7
- Confidence intervals for
- a single proportion
- difference between two proportions
- Hypothesis testing for
- a single proportion
- difference between two proportions
- Sample size calculations for studies of one or two proportions
1.8 Lecture 8
- Odds ratio
- Risk ratio (or relative risk)
- Number needed to treat
- Chi-squared test
- Fisher’s exact test
1.9 Lecture 9
- Hypothesis tests
- Sign test
- Signed rank test
- Rank Sum test
- Bootstrap confidence intervals
1.10 Lecture 10
- One-way ANOVA
- Estimation and checking of assumptions
- Multiple comparisons
- ANOVA in R
1.11 Lecture 11
- Extension of one-way ANOVA
- Randomized block design (or Repeated Measures)
- Two-way ANOVA
- Correlation
- Correlation vs. Causation
- Inference for the correlation coefficient
1.12 Lecture 12
- Simple and Multiple Linear Regression