Biostatistical Methods II: Logistic Regression and Survival Analysis
BIOL-40316
This fully online, self-paced course equips learners with powerful techniques in logistic regression and survival analysis, essential for clinical data analysis and biostatistical modeling in biomedical and health sciences.
Course Highlights:
- Advanced Statistical Methods: Master logistic regression for analyzing binary outcomes, such as disease diagnosis, and survival analysis for time-to-event data, like patient survival, with applications in clinical trials and public health.
- Practical Techniques: Learn to apply Kaplan-Meier estimators, Cox proportional hazards models, and logistic regression using R programming for robust data analysis.
- Hands-On Learning: Utilize biostatistics in R to analyze real-world datasets, interpreting results with diagnostic tools like likelihood ratio tests and Schoenfeld residuals.
- Flexible Format: Access course materials at your convenience during the session, accommodating professional or academic schedules.
Participants will gain expertise in designing, implementing, and interpreting logistic regression models and survival analyses, preparing them for careers in biostatistics, clinical research, or data science. This course builds on foundational knowledge, paving the way for specialized topics in future studies.
Who Should Enroll:
- Students seeking a logistic regression course or survival analysis training to advance academic research.
- Professionals aiming to enhance clinical data analysis skills for healthcare or pharmaceutical roles.
- Individuals interested in advanced biostatistics with applications in medical and public health research.
- Course Materials: A course reader is provided with each module, accessible to students at no additional cost. Instructions for accessing these materials are shared on the first day.
- Prerequisites: Completion of Biostatistical Methods I: Linear Regression and ANOVA or equivalent knowledge of linear regression and R programming is required.
Course Information
Course sessions
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
All course materials are included unless otherwise stated.
Policies:
- No refunds after: 9/29/2025
Schedule:
Instructor: Arthur Li, MS
Biostatistician, City of Hope; Instructor, Department of Preventative Medicine, USC
Arthur Li holds an M.S. in Biostatistics from the University of Southern California and serves as a biostatistician at City of Hope National Medical Center, where he supports cancer research by analyzing clinical and genomic data. At USC, he developed and taught SAS and R programming courses and occasionally taught a linear regression course, helping students build data analysis skills. At UC San Diego Division of Extended Studies, Li developed and teaches the Biostatistical Methods series courses, transitioned from SAS to R, assisting learners in exploring biostatistics, alongside other R programming courses. He authored the Handbook of SAS® DATA Step Programming (CRC Press, 2013), a resource for data management in SAS. In his spare time, Li enjoys traveling, cooking, and exploring new cultures.
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
All course materials are included unless otherwise stated.
Policies:
- No refunds after: 1/19/2026
Schedule:
Instructor: Arthur Li, MS
Biostatistician, City of Hope; Instructor, Department of Preventative Medicine, USC
Arthur Li holds an M.S. in Biostatistics from the University of Southern California and serves as a biostatistician at City of Hope National Medical Center, where he supports cancer research by analyzing clinical and genomic data. At USC, he developed and taught SAS and R programming courses and occasionally taught a linear regression course, helping students build data analysis skills. At UC San Diego Division of Extended Studies, Li developed and teaches the Biostatistical Methods series courses, transitioned from SAS to R, assisting learners in exploring biostatistics, alongside other R programming courses. He authored the Handbook of SAS® DATA Step Programming (CRC Press, 2013), a resource for data management in SAS. In his spare time, Li enjoys traveling, cooking, and exploring new cultures.
