Courses Taught by Xu Shi

BIOSTAT602: Biostatistical Inference

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 4 credit hour(s) for residential students;
  • Instructor(s): Xu Shi, Fan Bu, (Residential);
  • Prerequisites: Biostat 601
  • Description: Fundamental theory that is the basis of inferential statistical procedures. Point and interval estimation, sufficient statistics, hypothesis testing, maximum likelihood estimates, confidence intervals, criteria for estimators, methods of constructing test and estimation procedures.
  • Syllabus for BIOSTAT602
ShiXu
Xu Shi
BuFan
Fan Bu
Concentration Competencies that BIOSTAT602 Allows Assessment On
Department Program Degree Competency Specific course(s) that allow assessment
BIOSTAT MS Derive the theoretical mathematics of statistical inferences BIOSTAT602

BIOSTAT830: Advanced Topics in Biostatistics

  • Graduate level
  • Residential
  • Fall term(s) for residential students;
  • 1-4 credit hour(s) for residential students;
  • Instructor(s): Xu Shi (Residential);
  • Prerequisites: course/instructor dependent
  • Description: Advanced training in biostatistical methods primarily for doctoral students. Format will include lectures, readings, presentations and discussions in an area of special interest to students and faculty, such as stopping rules and interim analysis in clinical trials, conditional and unconditional inference and ancillarity, or nonparametric regression.
  • Syllabus for BIOSTAT830
ShiXu
Xu Shi

EPID731: Analysis Of Electronic Health Record (ehr) Data

  • Graduate level
  • Residential
  • Summer term(s) for residential students;
  • 1 credit hour(s) for residential students;
  • Instructor(s): Xu Shi (Residential);
  • Prerequisites: None
  • Advisory Prerequisites: Quantitative training, familiarity with traditional regression methods, basic epidemiologic principles, and working knowledge of R. The course will be instructed with minimal mathematics formulas and will include comprehensive examples to facilitate a bro
  • Undergraduates are allowed to enroll in this course.
  • Description: To gain knowledge of the process of cleaning and abstracting EHR data to create analytic datasets, attain a broader understanding the secondary use of EHR data for research, with a focus on epidemiologic principles including the role of study design, bias, and generalizability
  • Learning Objectives: This short course will offer an overview of modern analytical methods and research applications using EHR data, with a specific focus on epidemiologic inferences. Upon completion of the course, participants will i) gain knowledge of the process of cleaning and abstracting EHR data to create analytic datasets, ii) attain a broader understanding of the opportunities and challenges of the secondary use of EHR data for research, with a focus on epidemiologic principles including the role of study design, bias, and generalizability, iii) explore and gain hands-on experience using EHRs from Michigan Medicine, and iv) be prepared to generate and further explore new questions and perspectives.
ShiXu
Xu Shi