Recent progress in the understanding of HIV disease has suggested new research directions that pose complex challenges for design and analysis of HIV clinical trials. We propose to develop new statistical methods for both novel and recurring such problems. Although the pretest-posttest comparison is ubiquitous in HIV and other research, there is still no consensus on the relative merits of various methods of analysis, so that inefficient approaches are often used. The first part of this research focuses on developing a general theory for inference in this setting that leads to methods that improve over current approaches and provide the data-analyst with a clear strategy for choosing the most suitable method for a given application. Because missing responses, particularly at follow-up, are common in this context, the third part of the research involves extensions to take appropriate account of missing data. Increasingly, HIV clinical trials are focused on strategies where subjects may be treated through a series of stages; e.g., studies of structured treatment interruption involve similar considerations. However, ad hoc approaches to design and analysis are used due to absence of an appropriate methodology. The second part of the research is devoted to development of formal statistical framework for such complex studies that allows unambiguous statement of and valid inference on scientific questions of interest. Considerable interest in HIV research has focused on characterizing the relationship between longitudinal patterns of biomarkers, such as viral load or CD4, and measures of clinical progression, so much recent attention has focused on so-called joint models for longitudinal and event time data. In the final part of the research, we will carry out a detailed study of assumptions underlying this enterprise, clarify the practical implications of such assumptions, evaluate robustness to departures from assumptions, and develop a new approach for joint models that may improve over existing methods.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37AI031789-14
Application #
6766880
Study Section
AIDS and Related Research 8 (AARR)
Program Officer
Gezmu, Misrak
Project Start
1991-07-01
Project End
2008-02-29
Budget Start
2004-03-01
Budget End
2005-02-28
Support Year
14
Fiscal Year
2004
Total Cost
$324,128
Indirect Cost
Name
North Carolina State University Raleigh
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
042092122
City
Raleigh
State
NC
Country
United States
Zip Code
27695
Vock, David M; Durheim, Michael T; Tsuang, Wayne M et al. (2017) Survival Benefit of Lung Transplantation in the Modern Era of Lung Allocation. Ann Am Thorac Soc 14:172-181
Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel et al. (2016) Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules. J Biom Biostat 7:
Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel et al. (2015) Estimation After a Group Sequential Trial. Stat Biosci 7:187-205
Bernhardt, Paul W; Zhang, Daowen; Wang, Huixia Judy (2015) A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits. Comput Stat Data Anal 85:37-53
Bernhardt, Paul W; Wang, Huixia Judy; Zhang, Daowen (2014) Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation. Comput Stat Data Anal 69:
Verbeke, Geert; Fieuws, Steffen; Molenberghs, Geert et al. (2014) The analysis of multivariate longitudinal data: a review. Stat Methods Med Res 23:42-59
Schulte, Phillip J; Tsiatis, Anastasios A; Laber, Eric B et al. (2014) Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. Stat Sci 29:640-661
Vock, David M; Davidian, Marie; Tsiatis, Anastasios A (2014) SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models. J Stat Softw 56:2
Molenberghs, Geert; Kenward, Michael G; Aerts, Marc et al. (2014) On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: sequential trials, random sample sizes, and missing data. Stat Methods Med Res 23:11-41
Zhang, Baqun; Tsiatis, Anastasios A; Laber, Eric B et al. (2013) Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. Biometrika 100:

Showing the most recent 10 out of 53 publications