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Course

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.
Learning Outcomes:

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.
Additional Information:
 
  • 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

Online
3.00 units
$725.00
Notes: Instructions for ordering the course reader (e-textbook) will be provided on the first day of class.

Course sessions

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