Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling, Sensitivity Analysis, and Causal Inference

Transcription

1 COURSE: Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling, Sensitivity Analysis, and Causal Inference Mike Daniels (Department of Statistics, University of Florida) October 2011 Location: TBA Room: TBA Deadline for registration: 1 October For registration and further practical information, please contact Dymph Wijnen (d.wijnen@erasmusmc.nl), Department of Biostatistics, Erasmus MC, Dr. Molewaterplein 50, 3015 GE Rotterdam, Ee 2124 (21 st floor), tel:

2 Abstract This course provides a survey of primarily Bayesian approaches to handling missing data in longitudinal studies, and illustrates the use of newly-developed methods for model selection, sensitivity analysis, incorporation of prior information, and causal inference. The emphasis is on Bayesian approaches but the models and methods discussed can be implemented in non-bayesian settings as well. The course will be roughly divided into five parts: Part 1 of the course will include a brief review of models for longitudinal data and the basics of Bayesian inference. Part 2 will focus on formal classification of dropout and missing data mechanisms, describe classes of models that can be used to adjust for biases caused by dropout, and the logistics of model fitting and model selection and Bayesian proper imputation. Part 3 will deal with specification and fitting of models to handle non-ignorable (informative) dropout, with emphasis on the role of sensitivity analysis and informative prior distributions for encoding key assumptions. Part 4 will focus on causal inference in the context of incomplete longitudinal data. Part 5 will discuss approaches for handling missing time-varying and baseline covariates. Integrated into the course will be six case studies that illustrate many of the concepts introduced during the course. We will build on each case study to illustrate progressively more complex analyses (e.g. progressing from analysis under MAR, to analysis under MNAR, to use of informative priors and sensitivity analyses).

3 Suggested literature: Michael J. Daniels, Joseph W. Hogan: Missing Data in Longitudinal Studies. Strategies for Bayesian modeling and Sensitivity Analysis. CRC Chapman & Hall, 2008, 328 pp (ISBN ) Target Audience Professional statisticians working in applied environments where missing data is a key issue and where formal, well justified approaches are needed for making informed inferences; e.g. academic centers running large clinical trials, statisticians working in the pharmaceutical industry, statisticians working for regulatory agencies Researchers and students from statistics and related fields who are interested in the topic as an area of research. Necessary background for the course The necessary background is a working knowledge of linear and generalized linear models and basics of likelihood-based inference. This would include individuals with graduate degrees in statistics, biostatistics, econometrics and related fields, and advanced students in programs offering these degrees.

4 About the course instructor Michael Daniels is Professor and Chair in the Department of Statistics at the University of Florida. Mike has published extensively in the statistical literature on methods for (incomplete) longitudinal data with articles appearing in Biometrics, Biometrika, Biostatistics, Journal of the American Statistical Association, and Statistics in Medicine and has had continuous research funding from the U.S. National Institutes of Health (NIH) since He recently completed a book, coauthored with Joe Hogan, titled "Missing Data in Longitudinal Studies: Strategies for Bayesian modeling and Sensitivity Analysis". He has taught a graduate-level course on incomplete longitudinal data at the University of Florida several times and has given several short courses on missing data and dropout at national and international conferences and government agencies.

5 OUTLINE Day 1 9:00 10:45 Motivating examples, regression for longitudinal data, key concepts in Bayesian inference 10:45-11:00 Coffee break 11:00-12:30 Missing data mechanisms in longitudinal studies, Bayesian approaches to model selection and checking for incomplete longitudinal data 12:30-13:30 Lunch 13:30-15:00 Models and methods for ignorable missingness 15:00-15:15 Coffee break 15:15-17:00 Proper Bayesian multiple imputation, models for handling nonignorable missingness

6 Day 2 9:00 11:00 Sensitivity analysis and informative priors Part I 11:00-11:30 Coffee break 11:30 12:30 Sensitivity analysis and informative priors Part 2 12:30-13:30 Lunch 13:30-15:00 Causal inference and missing data in longitudinal studies 15:00-15:15 Coffee break 15:15-17:00 Missing covariates

7 Administrative information Coffee breaks 2 coffee breaks (one in morning and one in afternoon) are foreseen and are included in the registration costs. Lunch is not included. Course materials Copies of the slides used in the course are included in the registration costs. Also a website is available with some computer code. Costs Erasmus University: 150,-, other universities/governmental: 250,- commercial organizations: 750,-. Registration is only effective upon receipt of payment. For payment send us your invoice address and for Erasmus personnel also the kostenplaats.