Understanding clinical study results

The course is designed for healthcare professionals actively involved in clinical research projects who seek or need to improve their ability to interpret clinical research results comprehensively.

The intensive course program is divided into four modules. Some modules focus on result interpretation, while others address study design, hypotheses, and objectives.

All topics discussed will be illustrated using real studies—either those provided by participants or IBA’s case studies.

The course begins by introducing key biostatistics principles essential for study design, evaluation, and interpretation. We’ll discuss statistics for describing data, distinguishing between categorical and quantitative variables. In the second part, participants will become familiar with significance levels, test power, and p-values, and learn how to determine sample size. We will then explore statistical methods for evaluating primary and secondary study endpoints—including statistical tests, correlation analyses, contingency tables (OR, RR, NNT), and Kaplan-Meier curves with hazard ratios (HR). Finally, participants will learn to categorize studies according to various criteria, understand why randomization is critical, and what methods are suitable for non-randomized studies. Additionally, we’ll cover study objectives, superiority and non-inferiority settings, and subgroup analysis considerations.

The main goal of the course is to acquaint participants with statistical thinking principles, introduce statistical methods used in evaluating clinical studies, and equip them with skills for accurate and detailed data interpretation from scientific publications.

1. Data Description and Visualization

• Key biostatistics principles—bias, comparability, representativeness, significance, and reliability.
• Types of data—categorical and quantitative.
• Description and visualization of categorical data—counts, percentages, pie charts, bar charts.
• Description and visualization of quantitative data—mean, median, standard deviation, confidence intervals, quantiles, histograms, boxplots.

2. Principles of Statistical Reasoning

• Hypothesis testing.
• Type I and Type II errors, test power.
• Sample size optimization.
• Understanding p-values.

3. Statistical Methods Applied in Studies

• Parametric and non-parametric tests.
• Correlation analyses.
• Contingency tables—odds ratio (OR), relative risk (RR), number needed to treat (NNT).
• Diagnostic test indicators—sensitivity and specificity.

4. Clinical Studies and Statistical Evaluation

• Types of studies and study design.
• Study objectives.
• Superiority, non-inferiority, and equivalence studies.
• Randomization.
• Subgroup analysis.

The course will take place with a minimum of 3 participants. Should the minimum number not be reached, registered participants will be notified of course cancellation three weeks before the scheduled start date.