The purpose of this research is to develop new statistical methods for analyzing health related studies with missing data when the probability of non-response depends on the subject's unobserved measurements, i.e. when non-response is non-ignorable. Non-ignorable non-response is suspected, for example, in clinical studies of the elderly in which cognitive impaired subjects may be less likely to want or be able to participate, in quality of life studies in which worse off patients are less likely to be able to complete assessment tests, and in studies of socially sensitive outcomes like drug consumption or sexual behavior. The validity and usefulness of currently available methods for the analysis of non-ignorable non-response critically depends on highly restrictive parametric modeling assumptions likely to be violated in many health related studies. This research will develop semiparametric methods that are valid under less restrictive modeling assumptions and hence applicable to a wide spectrum of studies. This will entail: (1) the investigation of the identifiability of parameters of interest under specific semiparametric models, (2) the derivation of a comprehensive inferential approach when the parameters of interest are identifiable under the partially specified semiparametric models, (3) the derivation of efficient semiparametric estimators that effectively extract the information available in the observed data given the knowledge encoded in the semiparametric model. The methods derived in this work will be useful for conducting semiparametric sensitivity analyses. This work will also investigate methods for analyzing studies with follow-up of a, possibly biased, sample of non-respondents. Currently available parametric models for the analysis of continuous non-ignorable outcomes have a distinctive property that complicates the analysis: they are identifiable but have non-invertible Fisher information matrix. The distributional properties of the maximum likelihood estimators and of the likelihood ratio and score tests in these models are presently unknown. This research will derive the distributional theory for likelihood based inference in parametric identifiable models with non- invertible Fisher information.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM048704-06
Application #
2693934
Study Section
Special Emphasis Panel (ZRG7-STA (01))
Project Start
1994-01-01
Project End
2001-12-31
Budget Start
1998-01-01
Budget End
1999-12-31
Support Year
6
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
082359691
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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