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Biostatistics 140.652
Methods in Biostatistics II
Department of Biostatistics, Johns Hopkins
Bloomberg School of Public Health
Lecture Topics
1. Probability
- Basic Definitions and Axioms
- Conditional Probability and Independence
- Random Variables
- Bayes Theorem (sensitivity, specificity)
- Probability Distributions (discrete & continuous)
- Mean and Variance of a Distribution
2. Summarizing and Describing Data
- Summary Statistics (sample mean, median, variance)
- Graphical (histogram, box plots)
3. Inferences for One Sample
A. Inference for Means (and the Normal
Distribution)
-
Confidence intervals
-
Central limit
theorem
-
Hypothesis testing, significance levels, p-values, power
-
Sample size
considerations
-
Transformations, QQ
plots
B.
Inference for Proportions (and the Binomial Distribution)
4. The Two Sample Problem
- Comparing 2 means (paired versus independent samples)
- Comparing 2 proportions
- Sample size considerations
5. Contingency Tables
- 2 x 2 table: Fisher’s exact test and
X2 test
- r x c table
6. Statistical Methods in Epidemiology
- Prospective, case–control and cross-sectional studies
- Odds ratio and relative risk
- Simpson’s Paradox and Mantel-Haenszel test
- Matching and McNemar’s test
7. Introduction to Nonparametric Methods
- Signed rank test, Wilcoxon rank sum test, Kruskal Wallis test
- Advantages and disadvantages
- Measures of agreement
8. The Analysis of Count Data
- The Poisson distribution
- Inference based on count data
- Applications in epidemiology and bioassay
9. Issues and Controversies and Other Topics
- p-values and confidence intervals
- Frequentist, Bayesian & likelihood paradigms
- Introduction to the delta method and bootstrapping
- Multiple hypothesis testing
- Elements of good data presentation and analysis
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Last edited:
02 November, 2004
©2004,
Department of Biostatistics,
Johns Hopkins
Bloomberg School of Public Health
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