The purpose of this research is to extend my earlier research in non- and semi-parametric methods for the analysis of health studies with incomplete data, to methods for addressing a number of important unsolved problems for drawing causal inferences-from complex longitudinal observational and randomized studies with time-varying exposures or treatments. In this regard, to reflect the extended work scope of this project, its title has been amended to """"""""Methods for Analysis with Missing and/or Censored Data and for Causal Inference"""""""". The proposed methodology adequately adjusts for time-dependent confounding factors; that is, for risk factors for outcomes that also predict subsequent treatment. The main emphasis is in the estimation of new classes of causal models that are specifically tailored to answer each of the causal inquiries in this grant.
The first aim i s to develop methodology for estimating, from observational data, optimal dynamic treatment regimes that use partial covariate information. I will derive estimators of the parameters of a new class of models, the """"""""dynamic regime structural models"""""""". The new models are specifically tailored to the estimation of optimal dynamic regimes out of a set of simple, realistically enforceable, regimes.
The second aim i s to develop methods for the testing and estimation, from observational studies, of the direct effect of a treatment on an outcome when a second treatment is fixed at a pre-specified dose regimen. The proposed models facilitate the conduct of a sensitivity analysis that quantifies how one's inference concerning the direct effect of a treatment of interest varies as a function of the magnitude of confounding due to unmeasured factors.
The third aim i s to develop methods for estimating, from a double-blind randomized trial, treatment effects in the subset of the population in which a post-randomization event would occur under both treatments.
Tfte fourth aim i s to develop a theory of efficient, non-root-n consistent estimation based on higher order influence functions. This theory extends the theory of semi-parametric efficient root-n consistent estimation to allow for the construction of non-root-n estimators that are consistent under generally weaker assumptions. This theory is particularly, useful for constructing honest confidence intervals for the average treatment effect in the presence of such high-dimensional pre-treatment confounding factors that root-n estimation of the treatment effect is precluded.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM048704-14
Application #
7478737
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Remington, Karin A
Project Start
1994-01-01
Project End
2010-07-31
Budget Start
2008-08-01
Budget End
2009-07-31
Support Year
14
Fiscal Year
2008
Total Cost
$334,412
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Lok, Judith J; DeGruttola, Victor (2012) Impact of time to start treatment following infection with application to initiating HAART in HIV-positive patients. Biometrics 68:745-54
Wang, Lu; Rotnitzky, Andrea; Lin, Xihong et al. (2012) Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer. J Am Stat Assoc 107:493-508
Wang, Lu; Rotnitzky, Andrea; Lin, Xihong et al. (2012) Rejoinder to comments on Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer. J Am Stat Assoc 107:518-520
Tchetgen Tchetgen, Eric J; Rotnitzky, Andrea (2011) Double-robust estimation of an exposure-outcome odds ratio adjusting for confounding in cohort and case-control studies. Stat Med 30:335-47
Hu, Tianle; Nan, Bin; Lin, Xihong et al. (2011) Time-dependent cross ratio estimation for bivariate failure times. Biometrika 98:341-354
Tchetgen Tchetgen, E J; Rotnitzky, A (2011) On protected estimation of an odds ratio model with missing binary exposure and confounders. Biometrika 98:749-754
Lok, Judith J; Bosch, Ronald J; Benson, Constance A et al. (2010) Long-term increase in CD4+ T-cell counts during combination antiretroviral therapy for HIV-1 infection. AIDS 24:1867-76
Rotnitzky, Andrea; Li, Lingling; Li, Xiaochun (2010) A note on overadjustment in inverse probability weighted estimation. Biometrika 97:997-1001
Tchetgen Tchetgen, Eric J; Robins, James M; Rotnitzky, Andrea (2010) On doubly robust estimation in a semiparametric odds ratio model. Biometrika 97:171-180
Wang, Lu; Rotnitzky, Andrea; Lin, Xihong (2010) Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations. J Am Stat Assoc 105:1135-1146

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