Skip to main content
An official website of the United States government
Grant Details

Grant Number: 5R44CA088754-03 Interpret this number
Primary Investigator: Chao, Edward
Organization: Insightful Corporation
Project Title: Efficient Statistical Algorithms for Dropout Data
Fiscal Year: 2004


DESCRIPTION (provided by applicant): 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.


Semiparametric approaches for joint modeling of longitudinal and survival data with time-varying coefficients.
Authors: Song X. , Wang C.Y. .
Source: Biometrics, 2008 Jun; 64(2), p. 557-66.
EPub date: 2007-08-28.
PMID: 17725812
Related Citations

Expected estimating equations for missing data, measurement error, and misclassification, with application to longitudinal nonignorable missing data.
Authors: Wang C.Y. , Huang Y. , Chao E.C. , Jeffcoat M.K. .
Source: Biometrics, 2008 Mar; 64(1), p. 85-95.
EPub date: 2007-06-30.
PMID: 17608787
Related Citations

Numerical equivalence of imputing scores and weighted estimators in regression analysis with missing covariates.
Authors: Wang C.Y. , Lee S.M. , Chao E.C. .
Source: Biostatistics (Oxford, England), 2007 Apr; 8(2), p. 468-73.
EPub date: 2006-09-12.
PMID: 16971377
Related Citations

Back to Top