Unit 1620 - Biostatistics for Public Health

1. Introduction to Biostatistics

• Role of biostatistics in public health research and practice
• Basic statistical concepts: populations, samples, parameters, statistics
• Types of data: qualitative vs quantitative, nominal, ordinal, interval, ratio
• Importance of statistical analysis in decision-making and policy in public health

2. Descriptive Statistics

2.1 Measures of Central Tendency

• Mean, median, mode: definitions and when to use each
• Calculations and interpretation

2.2 Measures of Variability

• Range, variance, standard deviation: definitions and computation
• Understanding data spread and dispersion

2.3 Graphical Representations

• Histograms, bar charts, pie charts
• Box plots and stem-and-leaf plots
• Scatterplots for bivariate data

3. Probability and Probability Distributions

3.1 Fundamentals of Probability Theory

• Basic probability rules and concepts
• Conditional probability and independence

3.2 Probability Distributions

• Binomial distribution: assumptions and applications
• Normal distribution: properties and standard normal curve
• Poisson distribution: characteristics and use cases

3.3 Applications in Public Health

• Modeling disease occurrence and risk
• Decision-making under uncertainty

4. Confidence Intervals

• Concept and interpretation of confidence intervals
• Calculating confidence intervals for means and proportions
• Confidence intervals for differences between means
• Factors affecting width of confidence intervals

5. Hypothesis Testing

5.1 Fundamentals

• Null and alternative hypotheses
• Significance levels (alpha), p-values
• Type I and Type II errors

5.2 Application in Public Health Studies

• One-sample and two-sample tests
• Tests for proportions and means
• Using hypothesis testing to evaluate interventions

6. Chi-Square Tests

• Overview of categorical data analysis
• Chi-square test for independence
• Chi-square goodness-of-fit test
• Assumptions and interpretation
• Applications in public health research

7. Correlation and Regression Analysis

7.1 Correlation

• Pearson correlation coefficient: calculation and interpretation
• Limitations of correlation

7.2 Regression Analysis

• Simple linear regression: model, assumptions, interpretation
• Multiple regression: introduction and applications
• Using regression for prediction and controlling confounders

8. Epidemiological Study Designs

• Cross-sectional studies: characteristics and uses
• Case-control studies: design and strengths/limitations
• Cohort studies: prospective and retrospective
• Experimental studies: randomized controlled trials
• Choosing appropriate study designs in public health research

9. Sampling Techniques

• Simple random sampling
• Stratified sampling: rationale and implementation
• Cluster sampling
• Systematic sampling
• Importance of sampling for representativeness and validity

10. Survival Analysis

• Introduction to time-to-event data
• Kaplan-Meier survival curves: estimation and interpretation
• Log-rank test for comparing survival curves
• Cox proportional hazards model: concept and applications
• Using survival analysis in public health research

11. Meta-Analysis

• Concept and purpose of meta-analysis
• Steps in conducting a meta-analysis
• Assessing heterogeneity and publication bias
• Importance in synthesizing evidence for public health policy