MedStat

This course is ideal for:

• Clinicians and healthcare professionals aiming to publish in peer-reviewed medical journals.
• Postgraduate researchers analysing clinical or biomedical datasets.
• Undergraduate medical and health science students seeking to strengthen their sta0s0cal and research competency.
• Academics and clinical investigators who wish to confidently interpret data, evaluate research outcomes, and critically appraise scientific literature.

Whether you are preparing a manuscript, planning a study, or interpreting published evidence, this course will empower you to approach medical statistics with clarity, accuracy, and confidence.

By the end of this course, participants will be able to:

• Develop focused and scientifically valid research questions and corresponding hypotheses
• Summarize, visualize, and interpret data using appropriate descriptive statistics and graphical methods
• Understand and differentiate between key study designs and select the most suitable methodology for their research goals
• Identify and minimize bias by designing methodologically rigorous clinical studies
• Calculate and justify appropriate sample sizes to detect clinically meaningful outcomes
• Prepare datasets for statistical analysis, including managing missing data, outliers, and data transformations
• Distinguish between parametric and non-parametric tests and apply them appropriately
• Accurately interpret statistical results and translate findings into meaningful clinical insights
• Critically appraise published research using evidence-based statistical evaluation techniques
• Confidently perform statistical analyses using SPSS through guided, real-world practical exercises

Morning Session

• The essential role of medical statistics in clinical research and decision making
• Main Principles underpinning Statistical Analysis:
  • Probability theory and probability distributions
  • The Normal distribution and its applications
  • Statistical inference and hypothesis testing principles
  • Sampling distribution, standard error, confidence intervals, and significance levels
• Introduction to SPSS interface and workflow
• Descriptive statistics for summarizing clinical data
• Data visualization techniques for meaningful interpretation
• Practical Session: Generating and interpreting descriptive statistics in SPSS

Coffee Break

Second Session

• Choosing the right study design: Understanding strengths, limitations, and real-world applicability
• Preparing clinical data for analysis:
  • Identifying and addressing missing data
  • Detecting and managing outliers
  • Testing for normality
• Hypothesis testing:
  • The Null hypothesis
  • Test statistics, effect sizes, p-values, and sample size considerations
  • Type I and Type II errors and their implications
• Comparing Continuous Variables:
  • Parametric Tests:
    • Independent samples t-test
    • Paired samples t-test
  • Non-Parametric Alternatives:
    • Mann-Whitney U test
    • Wilcoxon Signed-Rank test
• SPSS Practical: Performing t-tests and interpreting output with clinical relevance

Lunch Break

Afternoon Session

• Comparing Categorical Variables:
  • Chi-Square Test
  • Fisher’s Exact Test
• SPSS Practical: Performing and interpreting categorical data analysis
• Correlation & Regression
  • Pearson’s Correlation
  • Spearman’s Correlation
  • Regression Models
    • Linear Regression
    • Multiple Linear Regression
    • Multicollinearity
• Clinical Risk Analysis and Association Measures
  • Incidence vs. prevalence
  • Risk ratio (relative risk)
  • Odds ratio and adjusted odds ratio
  • Adjusted Odds Ratio using Logistic Regression
• SPSS Practical: Building a Logistic Regression Model and Interpreting Odds Ratios
• Survival Analysis:
  • Kaplan-Meir Survival Method