This project will pursue statistical methods in several main areas of AIDS research. The sense of urgency surrounding the AIDS crisis has led to the need for methods enabling more effective and efficient design and analysis of clinical trials. To reduce the time and resources required to conduct studies, multivariate extensions of group sequential designs will be developed using counting process tools, and the role surrogates for long term clinical efficacy measures will be investigated. New robust nonparametric methods for analysis of mismeasured or missing covariate data will be explored. Estimation of HIV prevalence and projection of AIDS epidemic parameters improves the understanding of the dynamics of the epidemic and provides a basis for predicting its future course for public health planning purposes. Precise estimates of prevalence will be obtained by developing statistical methods for assays performed on pooled samples, through use of Polymerase Chain Reaction Assays, and by using small area estimation techniques. Novel statistical methods based on a pseudo-likelihood function will be developed for the estimation of various parameters of the AIDS epidemic, when one uses HIV prevalence data augmented by parameter estimates from other sources of data. All proposed methods will be implemented in actual AIDS research data sets. The clinical and psychological course for ARc and AIDS patients entering the health care system can be quite complex. Flexible statistical methodology will be explored which will allow a better understanding of this course through the analysis of data arising in registries and in clinical trails. Of particular interest will be multivariate point process models which allow time dependent covariates. There are particularly compelling reasons to obtain approaches to insuring security of confidential AIDS research and patient care data. Two probabilistic data security approaches will be developed, based on hash code identifier methods and partial information file matching methods.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI029168-05
Application #
3143939
Study Section
AIDS and Related Research Study Section 2 (ARRB)
Project Start
1989-09-30
Project End
1995-07-31
Budget Start
1993-08-01
Budget End
1994-07-31
Support Year
5
Fiscal Year
1993
Total Cost
Indirect Cost
Name
University of Washington
Department
Type
Schools of Public Health
DUNS #
135646524
City
Seattle
State
WA
Country
United States
Zip Code
98195
Fleming, Thomas R; Ellenberg, Susan S; DeMets, David L (2018) Data Monitoring Committees: Current issues. Clin Trials 15:321-328
Chung, Yunro; Ivanova, Anastasia; Hudgens, Michael G et al. (2018) Partial likelihood estimation of isotonic proportional hazards models. Biometrika 105:133-148
Wakefield, Jon; Fuglstad, Geir-Arne; Riebler, Andrea et al. (2018) Estimating under-five mortality in space and time in a developing world context. Stat Methods Med Res :962280218767988
Halloran, M Elizabeth; Auranen, Kari; Baird, Sarah et al. (2017) Simulations for designing and interpreting intervention trials in infectious diseases. BMC Med 15:223
Jiang, Runchao; Lu, Wenbin; Song, Rui et al. (2017) DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY. Ann Appl Stat 11:1763-1786
Fleming, Thomas R; DeMets, David L; Roe, Matthew T et al. (2017) Rejoinder. Clin Trials 14:126-127
Mao, Lu; Lin, D Y (2017) Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks. J R Stat Soc Series B Stat Methodol 79:573-587
Fleming, Thomas R; Demets, David L; McShane, Lisa M (2017) Discussion: The role, position, and function of the FDA-The past, present, and future. Biostatistics 18:417-421
Zeng, Donglin; Gao, Fei; Lin, D Y (2017) Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data. Biometrika 104:505-525
Li, Quefeng; Yu, Menggang; Wang, Sijian (2017) A Statistical Framework for Pathway and Gene Identification from Integrative Analysis. J Multivar Anal 156:1-17

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