June 17-18, 2024 | In-person training

The Statistical Analysis with Missing Data Workshop is a two-day intensive workshop of seminars and hands-on analytical sessions to provide an overview of concepts, methods, and applications for statistical analysis of health studies with missing data.

This two-day intensive workshop integrates the principle concepts and methods commonly used in statistical analysis with missing data and their applications in surveys, longitudinal studies, and clinical trials. Led by a team of renowned experts in missing data research, this workshop will integrate seminar lectures with hands-on computer lab sessions and case studies to put concepts into practice. We will cover weighting, maximum likelihood, Bayes, and multiple imputation methods and use a wide variety of examples to illustrate the techniques and approaches. We will also discuss methods for missing not at random and the latest developments on missing data research.

By the end of the workshop, participants will be familiar with the following topics:

• Missing data patterns and mechanisms
• Weighting methods
• Maximum likelihood methods
• Bayes and multiple imputation
• Approaches to missing not at random
• Missing data in surveys
• Missing data in longitudinal studies
• Missing data in clinical trials

Audience and Requirements

Investigators from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this training:

1. Each participant must be familiar with common methods of statistical analysis of complete data, such as multiple regression and logistic regression.
2. Each participant must have experience with programming in R.
3. Each participant is required to bring a personal laptop as all lab sessions will be done on your personal laptop. Each participant must have R downloaded and installed prior to attending the Workshop.

Instructors

Roderick J. Little, PhD, University of Michigan School of Public Health. Roderick J. Little is Richard D.

Qixuan Chen, PhD, Mailman School of Public Health, Columbia University. Qixuan Chen is Associate Professor of Biostatistics at Columbia University.

Additional Information

• Subscribe for updates on new Training details and registration deadlines.
• Contact the Boot Camp team.

Capacity is limited. Paid registration is required to attend.