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CI722 Clinical Data Science: Comparative Effectiveness Research I

Semester: 

Spring

Offered: 

2020

Instructors: Barbra Dickerman, Brian Healy, Miguel Hernán

This course introduces causal inference methodology for settings in which randomized trials are not available. The course focuses on the use of epidemiologic studies, electronic health records and other sources of observational data for comparative effectiveness and safety research. These methods are...

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CI732 Clinical Data Science: Comparative Effectiveness Research II

Semester: 

Fall

Offered: 

2019

Instructors: Barbra Dickerman, Brian Healy, Miguel Hernán

 

This course completes the introduction to commonly used data analysis approaches and study designs for causal inference.  The course focuses on analysis of time-varying treatments, advanced study designs, and correlated outcomes.  The series will end with three special lectures from visionaries in the field of clinical data science.  In addition to the new topics covered in the course, students will conduct two projects to integrate the knowledge...

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CI708 Clinical Data Science: Design and Analytics II

Semester: 

Fall

Offered: 

2019

Instructors: Barbra Dickerman, Brian Healy, Miguel Hernán

This course extends the topics introduced in Design and Analytics I and continues to integrate epidemiology, biostatistics, and machine learning. All methods are taught along with R software to implement them. The course is structured around the three tasks of clinical research: description, prediction and causal inference. The description sessions discuss unsupervised learning with a focus on clustering.  The prediction sessions discuss building and evaluation of predictive models...

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CI701 Clinical Data Science: Design and Analytics I

Semester: 

Summer

Offered: 

2019

Instructors: Barbra Dickerman, Brian Healy, Miguel Hernán

Clinical research requires the generation and analysis of data for three tasks: description, prediction, and causal inference. This course introduces the methods to accomplish these tasks through seamless integration of materials usually taught separately in epidemiology, biostatistics, and machine learning...

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