Module 1: Introduction to Clinical Trials and Biostatistics
- Phases of clinical trials (I–IV)
- Role of biostatistics in clinical trials
- Regulatory guidelines (ICH-GCP, FDA, EMA)
- Basic statistical concepts and their relevance to clinical research (mean, SD, hypothesis testing)
Module 2: Study Design and Sample Size Determination
- Randomized controlled trials (RCTs), cross-over trials, observational studies
- Blinding and randomization techniques
- Sample size calculation for different endpoints (binary, continuous, survival)
- Power and sample size (hands-on in R or SAS)
Module 3: Statistical Methods in Clinical Trials
- Descriptive statistics in clinical trials
- Inferential statistics (t-tests, ANOVA, chi-square, etc.)
- Linear and logistic regression
- Repeated measures and mixed models
- Adjusting for multiplicity
Module 4: Analyzing and Reporting Clinical Trial Data
- Confidence intervals and hypothesis testing
- P-values and their limitations
- Interim analysis and adaptive trial designs
- Missing data handling (imputation methods)
- Data visualization (R/Excel)
Module 5: Survival Analysis
- Kaplan-Meier curves and log-rank tests
- Cox proportional hazards models
- Censoring and time-to-event data analysis
Module 6: Bayesian Statistics in Clinical Trials
- Basics of Bayesian statistics
- Comparison with frequentist methods
- Bayesian applications in trial design and decision-making
Module 7: Interpreting Statistical Reports & Regulatory Submissions
- Statistical analysis plan (SAP)
- Interpreting clinical study reports (CSRs)
- Regulatory submissions (FDA, EMA)
- Communicating statistical results to non-statisticians
Module 8: Applications and Hands-on Training
- Practical analysis using R, SAS, or Python
- Case studies on real clinical trial datasets
- Building dashboards for data visualization (e.g., R Shiny, Power BI)
Learning Outcomes
By the end of the course, candidates will:
- Understand the role of biostatistics in the clinical trial lifecycle.
- Apply statistical methods to analyze clinical trial data.
- Determine appropriate sample sizes and interpret p-values and confidence intervals.
- Perform survival and Bayesian analysis.
- Create statistical analysis plans and interpret clinical trial reports.
Applications used to Train Candidates
- Software: R, SAS, Power BI for dashboards.
- Real-world case studies in oncology, cardiology, or vaccines.
- Hands-on with simulated or de-identified clinical trial datasets.
Key Steps in Clinical Trials for Curriculum Coverage
- Trial Design: Cover randomization, blinding, and sample size determination.
- Protocol Development: Statistical sections in the protocol.
- Data Management and Monitoring: Missing data handling, interim analysis.
- Data Analysis: Inferential and survival statistics, reporting.
- Trial Reporting: Statistical interpretation for regulatory agencies.
Who this course is for?
Bachelor’s degree in related field like statistics, mathematics life sciences, pharmacy and are interested in applying statitical methods specifically to clinical research that includes professionals working in the pharmaceutical industry, researchers conducting biological studies or individuals looking to pursue a career in clinical trial design and analysis.
