Unit 1368: Biostatistics for Biological and Health Sciences

1. Introduction to Biostatistics

• Definition and scope of biostatistics
• Role of statistics in biological and health sciences
• Types of data in biostatistics: qualitative vs quantitative, discrete vs continuous
• Importance of statistical analysis in drawing scientific conclusions
• Basic principles and terminology in biostatistics

2. Descriptive Statistics in Biostatistics

• Measures of Central Tendency
  • Mean: calculation and interpretation
  • Median: use in skewed data
  • Mode: identification and relevance
• Measures of Dispersion
  • Range, variance, and standard deviation
  • Interquartile range (IQR)
• Graphical Representation of Data
  • Histograms, bar charts, pie charts
  • Box plots and scatter plots
  • Stem-and-leaf plots

3. Probability in Biostatistics

• Fundamental concepts of probability
• Rules of probability: addition and multiplication rules
• Conditional probability and independence
• Probability distributions relevant to biostatistics:
  • Discrete distributions: Binomial, Poisson
  • Continuous distributions: Normal, Exponential
• Applications of probability in biological and health data analysis

4. Sampling Techniques and Study Design

• Importance of sampling in biostatistics
• Sampling methods:
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Systematic sampling
• Sample size considerations
• Introduction to study designs:
  • Observational: cross-sectional, case-control, cohort
  • Experimental: randomized controlled trials
• Ensuring validity and reliability through design

5. Hypothesis Testing in Biostatistics

• Concept of hypothesis testing
• Null hypothesis (H0) vs alternative hypothesis (H1)
• Significance level (alpha) and p-values
• Type I and Type II errors
• Power of a test
• Steps in hypothesis testing
• Interpretation of results in biological and health contexts

6. Parametric and Nonparametric Tests

• Overview of parametric tests:
  • Student’s t-test (one-sample, independent, paired)
  • Analysis of Variance (ANOVA)
• Overview of nonparametric tests:
  • Chi-square test
  • Mann-Whitney U test
  • Wilcoxon signed-rank test
• Assumptions underlying parametric vs nonparametric tests
• Choosing appropriate tests based on data distribution and research objectives

7. Regression Analysis in Biostatistics

• Simple linear regression:
  • Model formulation and interpretation
  • Assumptions and diagnostics
• Multiple regression analysis:
  • Incorporating multiple predictors
  • Multicollinearity and model selection
• Logistic regression:
  • Binary outcome modeling
  • Odds ratios and interpretation
• Applications in biological and health studies

8. Survival Analysis

• Introduction to time-to-event data
• Kaplan-Meier survival curves:
  • Estimation and interpretation
  • Comparison of survival curves
• Cox proportional hazards model:
  • Model assumptions and hazard ratios
  • Adjusting for covariates
• Life tables and their use
• Applications in disease progression and mortality studies

9. Meta-Analysis and Systematic Reviews

• Definitions and purposes
• Steps in conducting systematic reviews
• Principles of meta-analysis:
  • Data synthesis from multiple studies
  • Fixed-effect vs random-effects models
• Assessing publication bias
• Interpretation and limitations
• Role in evidence-based healthcare

10. Application of Biostatistics in Public Health

• Use in epidemiology and disease surveillance
• Health policy evaluation through statistical methods
• Biostatistics in outbreak investigation and control
• Designing public health interventions based on statistical evidence
• Case studies highlighting biostatistics impact on population health