Statistical Analysis with Missing Data Workshop: Methods and Applications in Health Studies
Summer 2024 (Dates TBD) | In-person training
The next Statistical Analysis with Missing Data Workshop will be Summer 2024. Sign up below to hear about registration opening!
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.
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Summer 2024 dates: In-person training dates TBD; 9:00am - ~5:00pm EDT
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
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:
- 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.
Summer 2024 instructing team is being finalized, but will be comparable to the 2023 lineup below.
Roderick J. Little, PhD, University of Michigan School of Public Health. 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, PhD, Mailman School of Public Health, Columbia University. 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.
Training scholarships are available for the Statistical Analysis with Missing Data Workshop.
Summer 2024: The Statistical Analysis with Missing Data Workshop is a live, in-person training taking place Summer 2024 from 9am -~5pm EDT (dates TBD) at the Columbia University Irving Medical Campus in NYC. All training start and end times are in EDT.
"It was a great course taught by two enthusiastic super stars in the field! Highly recommend if you are currently working on datasets with a variety of missing patterns and would like a great applied workshop." - Postdoc at Columbia University Mailman School of Public Health, 2023
"It's a great workshop giving participants both a theoretical and applied understanding of methods to deal with missing data - I highly recommend it!" - Research scientist at Weill Cornell Medicine, 2023
"This was a thorough short course of the theory behind missing data analysis and practical code implementation. I feel more prepared to approach this problem in my datasets and look forward to investigating." - PhD candidate at Columbia University, 2023
|Early-Bird Rate (through 4/10/24)||Regular Rate (beginning 4/11/24)||Columbia Discount*|
|Faculty/Academic Staff/Non-ProfitOrganizations/Government Agencies||$1,395||$1,595||10%|
*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. If paying by credit card, use your Columbia email address during the registration process to automatically have the discount applied. If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.
Invoice Payment: If you would prefer to pay by invoice/check, please submit this Invoice Request form to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.
Registration Fee: Registration Fee includes course material, breakfast, and lunch on training days. Course material will be available to all attendees during and after the workshop. Lodging and transportation are not included.
Cancellations: Cancellation notices must be received via email at least 30 days prior to the training start date in order to receive a full refund, minus a $75 administrative fee. Cancellation notices received via email 14-29 days prior to the training will receive a 75% refund, minus a $75 administrative fee. Please email your cancellation notice to Columbia.StatisticalAnalysis@gmail.com. Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations <14days prior to the training.
If you are unable to attend the training, we encourage you to send a substitute within the same registration category. Please inform us of the substitute via email at least one week prior to the training to include them on attendee communications, updated registration forms, and materials. Should the substitute fall within a different registration category your credit card will be credited/charged respectively. Please email substitute inquiries to Columbia.StatisticalAnalysis@gmail.com. In the event Columbia must cancel the event, your registration fee will be fully refunded.
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The Statistical Analysis with Missing Data Workshop is hosted by Columbia University's SHARP Program at the Mailman School of Public Health.