Advanced Medical Statistics: Data Analysis for Evidence-based Decision Making

• Develop a deep understanding of advanced statistical concepts and techniques commonly used in clinical research.
• Learn statistical methods for analyzing categorical and continuous data, including regression analysis, survival analysis, and longitudinal data analysis.
• Gain proficiency in using statistical software to perform complex analyses and generate meaningful results.
• Understand the principles of data validation, quality control, and data management in clinical research.
• Apply advanced statistical methods in interpreting research findings, assessing treatment effects, and communicating statistical results effectively to various stakeholders

Week 1: Introduction to Medical Statistics and Research Design

• Overview of medical statistics and its role in clinical research
• Understanding different types of research designs and their implications for statistical analysis

Week 2: Descriptive Statistics and Data Presentation

• Calculation and interpretation of descriptive statistics (measures of central tendency, variability)
• Effective data presentation techniques for clinical research

Week 3: Probability and Probability Distributions

• Understanding probability theory and its applications in clinical research
• Study of common probability distributions (normal, binomial, Poisson)

Week 4: Statistical Inference and Hypothesis Testing

• Principles of statistical inference and hypothesis testing
• Performing t-tests, chi-square tests, and other parametric and non-parametric tests

Week 5: Confidence Intervals and Sample Size Determination

• Construction and interpretation of confidence intervals
• Sample size determination for clinical research studies

Week 6: Analysis of Variance (ANOVA)

• Introduction to ANOVA and its applications in clinical research
• Performing one-way and two-way ANOVA tests

Week 7: Linear Regression and Correlation Analysis

• Understanding the concepts of linear regression and correlation
• Analyzing the relationship between variables and interpreting regression coefficients

Week 8: Survival Analysis and Time-to-Event Data

• Introduction to survival analysis and Kaplan-Meier estimation
• Analyzing time-to-event data and understanding survival curves

Week 9: Meta-Analysis and Systematic Reviews

• Principles and methods of meta-analysis and systematic reviews
• Conducting a comprehensive literature review and synthesizing evidence

Week 10: Longitudinal Data Analysis

• Analysis of longitudinal data and repeated measures
• Mixed-effects models and other approaches for longitudinal analysis

Week 11: Advanced Statistical Techniques in Clinical Research

• Introduction to advanced statistical techniques (logistic regression, propensity score matching, hierarchical models)
• Application of advanced methods in clinical research scenarios

Week 12: Capstone Project and Course Review

• Completion of a capstone project demonstrating the application of statistical techniques in clinical research
• Review of key concepts, discussions, and wrap-up of the course

• Proficiency in advanced statistical methods used in clinical research.
• Ability to perform complex statistical analyses and interpret research findings.
• Skills in using statistical software for data analysis and visualization.
• Understanding of data validation, quality control, and management principles.
• Competence in contributing to evidence-based decision making in clinical practice.