Statistical Analysis with Missing Data
Master statistical analysis with missing data! Engage in seminars and hands-on sessions for concepts, methods, and applications in health studies.
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Course Description
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.
- Grasp common challenges in health research related to missing data, identifying patterns and mechanisms underlying missing data occurrences.
- Acquire proficiency in various statistical methods such as weighting, maximum likelihood, Bayes, and multiple imputation for handling missing data in surveys, longitudinal studies, and clinical trials.
- Engage in hands-on computer lab sessions and case studies, utilizing real-world examples to apply weighting, maximum likelihood, Bayes, and multiple imputation methods to address missing data challenges effectively.
- Delve into advanced topics including methods for missing not at random, latest developments in missing data research, and specific applications of missing data techniques in surveys, longitudinal studies, and clinical trials.
Course Prerequisites
- Each participant must be familiar with common methods of statistical analysis of complete data, such as multiple regression and logistic regression.
- Each participant must have experience with programming in R.
- 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.
What You Will Learn
Missing data is a common challenge in health research. Statistical methods and tools can be used to handle missing data to achieve valid statistical inference.
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
Instructors
Roderick J. Little is Richard D. Remington Distinguished University Professor of Biostatistics at the University of Michigan, where he also holds appointments in the Department of Statistics and the Institute for Social Research. From 2010-21012 he was the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau.
He has over 250 publications, notably on methods for the analysis of data with missing values and model-based survey inference, and the application of statistics to diverse scientific areas, including medicine, demography, economics, psychiatry, aging and the environment. His book "Statistical Analysis with Missing Data" with Donald Rubin is now in its 3rd edition, and has over 30,000 google scholar citations.
Little is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association and the American Academy of Arts and Sciences, and a member of the Institute of Medicine of the U.S. National Academies. In 2005, Little was awarded the American Statistical Association’s Wilks Medal for research contributions, and he gave the President’s Invited Address at the Joint Statistical Meetings. He was the COPSS Fisher Lecturer at the 2012 Joint Statistics Meetings.
Qixuan Chen is Associate Professor of Biostatistics at Columbia University. Her research focuses on statistical methods development for handling missing data and measurement error arising from health studies. She has also made important contributions in developing novel methods for the analysis of complex survey data. She has been actively engaged in building analysis tools to promote the use of novel statistical methods in health research, with applications to environmental health sciences, psychiatry and mental health, substance abuse, and traffic safety. She is an Associate Editor for Biometrics.