This course is a continuation of Statistics for Clinical Trials I, with a focus on statistical computation and data analysis techniques specifically tailored for clinical trials. Students will work with the R and SAS statistical programming languages to gain a comprehensive understanding of these methods in the clinical trials context. The primary goal is to equip graduate students with the statistical skills required for data analysis in clinical trials. Successful students will become proficient in using statistical computational tools to analyze real-world clinical datasets and will be exposed to advanced statistical techniques and best practices for data storage, management, and analysis. Key statistical topics covered in this course include sampling designs, chi-square tests, linear models, mixed-effects models for repeated measurements and survival analysis. Prerequisite: STAT 514. Notes: Students outside of the course-based MSc with a specialization in Biostatistics need permission from the Department to enroll in this course. Thesis-based graduate students in Mathematical and Statistical Sciences cannot take this course for credit.