This research will develop a statistical software library in S-PLUS for dropout data. Missing and dropout data are common nature in longitudinal studies. When the dropout process is related to the outcome process, it creates tremendous challenges in analyzing such data. No commercial software currently considers the dropout mechanisms in dealing with informative or non-random dropout. Consequently, the results are biased and misleading. The ultimate objective of this research is the development of a statistical software library for analyzing dropout data using both pattern mixture and selection model approaches. The approaches apply linear models, generalized linear mixed-effects models or GEE models for the response process and a regression using a Iogit, a probit or a Clog-log link for the dropout process. This library will include methods for parameter estimation, sensitivity analysis, graphical analysis, and model selection. The algorithms developing for parameter estimation include stochastic EM, likelihood maximization and imputation methods. Graphical tools will be developed for displaying dropout data, monitoring parameter convergence and diagnosing fitted values. Sensitivity analysis based on analytic and graphic methods are useful on testing the validity of the modeling assumptions. Comprehensive case studies and simulations will show the advantage and the applicability of the results of this investigation. ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
5R44CA088754-03
Application #
6744309
Study Section
Special Emphasis Panel (ZRG1-EDC-2 (10))
Program Officer
Choudhry, Jawahar
Project Start
2000-09-29
Project End
2006-04-30
Budget Start
2004-05-01
Budget End
2006-04-30
Support Year
3
Fiscal Year
2004
Total Cost
$382,467
Indirect Cost
Name
Insightful Corporation
Department
Type
DUNS #
150683779
City
Seattle
State
WA
Country
United States
Zip Code
98109