Browsing by Author "Daowen Zhang, Member"
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- Accelerated Failure Time Model for Correlated Survival Data: Efficient Estimation and Inference.(2012-04-25) Liu, Bo; Wenbin Lu, Chair; Anastasios Tsiatis, Member; Daowen Zhang, Member; Arnab Maity, Member; John Gilligan, Graduate School Representative
- Advanced Statistical Methods for Complex Longitudinal Data.(2012-05-02) Vock, David Michael; Anastasios Tsiatis, Co-Chair; Marie Davidian, Co-Chair; Daowen Zhang, Member; Dennis Boos, Member; James Guy, Graduate School Representative
- Advances in Policy Evaluation and Learning: Targeting, Truncation by Death, and One-sided Feedback.(2024-08-09) Chu, Jianing; Wenbin Lu, Co-Chair; Shu Yang, Co-Chair; Marie Davidian, Member; Daowen Zhang, Member; Onkar Malgonde, Graduate School Representative
- Dose-Response and Generalized Additive Models for Open Label Trials: Applications to a Noise Aversion Study in Dogs.(2022-08-05) Zhao, Jiashu; Emily Griffith, Chair; Margaret Gruen, Member; Daowen Zhang, Member
- Doubly-robust Estimators in Observational Studies with and without a Stratified Sub-sample.(2014-03-14) Bai, Xiaofei; Anastasios Tsiatis, Chair; Negash Medhin, Graduate School Representative; Marie Davidian, Member; Wenbin Lu, Member; Daowen Zhang, Member
- Estimating Causal Treatment Effects via the Propensity Score and Estimating Survival Distributions in Clinical Trials That Follow Two-Stage Randomization Designs(2001-08-15) Lunceford, Jared Kenneth; Marie Davidian, Chair; Anastasios Tsiatis, Member; Dennis Boos, Member; Daowen Zhang, MemberEstimation of treatment effects with causalinterpretation from obervational data is complicated by the fact thatexposure to treatment is confounded with subject characteristics. Thepropensity score, the probability of exposure to treatment conditionalon covariates, is the basis for two competing classes of approachesfor adjusting for confounding: methods based on stratification ofobservations by quantiles of estimated propensity scores, and methods based on weighting individual observations by weights depending onestimated propensity scores. We review these approaches andinvestigate their relative performance.Some clinical trials follow a design in which patientsare randomized to a primary therapy upon entry followed by anotherrandomization to maintenance therapy contingent upon diseaseremission. Ideally, analysis would allow different treatmentpolicies, i.e. combinations of primary and maintenance therapy ifspecified up-front, to be compared. Standard practice is to conductseparate analyses for the primary and follow-up treatments, which doesnot address this issue directly. We propose consistent estimators ofthe survival distribution and mean survival time for each treatmentpolicy in such two-stage studies and derive large sampleproperties. The methods are demonstrated on a leukemia clinical trialdata set and through simulation.
- Flexible Estimation and Testing Methods for Survival Data with Application in Epidemiology and Precision Medicine.(2017-04-28) Kang, Suhyun; Wenbin Lu, Chair; Daowen Zhang, Member; Anastasios Tsiatis, Member; Rui Song, Member; Mitzi Stumpf, Graduate School Representative
- High Dimensional Methods in Statistics, Data Mining and Finance.(2014-08-14) Sahoo, Saswata; Soumendra Lahiri, Chair; Gregory Dawes, Graduate School Representative; Lexin Li, Member; Daowen Zhang, Member; Howard Bondell, Member
- Information-based Group Sequential Tests With Lagged or Censored Data(2000-06-23) Kung, Meifen; Anastasios A. Tsiatis, Chair; Dennis Boos, Member; William Swallow, Member; Daowen Zhang, MemberConventionally, values of nuisance parameters given in a statistical design are often erroneous, thus may result in overpowering or underpowering a test using traditional sample size calculations. In this thesis, we propose to use Fisher Information data monitoring in group sequential studies to not only allow an early stopping in a clinical trial but also maintain the desired power of the test for all values of nuisance parameters. Simulation studies for the simple case of comparing two response rates are used to demonstrate that a test of a single parameter of interest with a specified alternative achieves the desired power in information-based monitoring regardless of the value of the nuisance parameters, provided that this parameter of interest can be estimated efficiently. The emphasis in this part is to show how information-based monitoring can be implemented in practice and to demonstrate the accuracy of the corresponding operating characteristics in some simulation studies.When there is lag time in reporting, standard statistical techniques often lead to biased inferences on interim data. A maximum lag estimator ensures complete information by using data before a lag time period. The estimator is unbiased but less powerful. We propose an inverse probability weighted estimator which accounts for censoring and is consistent and asymptotically normal in estimating mean of dichotomous variables. The joint distribution of test statistics at different times have the covariance structure of a sequential process with independent increments. This allows the use of information-based monitoring. Simulation study shows that our estimator preserves the type I and type II errors, and reduces the number of participants required in a trial. Future approach in finding an efficient estimator is also suggested in chapter 3.
- New Techniques for Functional Data Analysis: Model Selection, Classification, and Nonparametric Regression.(2012-08-15) Avery, Matthew Rogers; Hao Zhang, Co-Chair; Yichao Wu, Co-Chair; Jorge Piedrahita, Graduate School Representative; Daowen Zhang, Member; Ana-Maria Staicu, Member
- On Estimation of Contagion-based Social Network Dependence with Event Time Data.(2018-04-30) Yu, Lin; Wenbin Lu, Chair; Jeffrey Mielke, Graduate School Representative; Rui Song, Member; Daowen Zhang, Member; Soumendra Lahiri, Member
- Optimal Dynamic Treatment Regimes from a Classification Perspective for Two Stage Studies with Survival Data.(2016-10-19) Hager, Rebecca Sarah; Marie Davidian, Co-Chair; Anastasios Tsiatis, Co-Chair; Daowen Zhang, Member; Eric Laber, Member; Osman Ozaltin, Graduate School Representative
- Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.(2012-03-02) Schulte, Phillip; Marie Davidian, Co-Chair; Anastasios Tsiatis, Co-Chair; Wenbin Lu, Member; Daowen Zhang, Member; Eric Laber, Member; Robin Gardner, Graduate School Representative
- Robust Causal Inference Methods for Using Randomized Clinical Trial and Observational Study.(2022-10-24) Cho, Eunah; Shu Yang, Chair; Daowen Zhang, Member; Marie Davidian, Member; Luo Xiao, Member; Ilze Kalnina, Graduate School Representative
- Robust Methods in Kernel Association Testing.(2020-03-06) Martinez, Kara; Arnab Maity, Chair; Daowen Zhang, Member; Jung-Ying Tzeng, Member; Wenbin Lu, Member; Roby Sawyers, Graduate School Representative
- Robust Statistical Method for Estimating Optimal Dynamic Treatment Regimes.(2012-06-29) Zhang, Baqun; Anastasios Tsiatis, Chair; Marie Davidian, Co-Chair; Daowen Zhang, Member; Eric Laber, Member; Zhilin Li, Graduate School Representative
- Semiparametric Bayesian Quantile Regression.(2014-07-22) Jang, Woo Sung; Huixia Wang, Chair; Sujit Ghosh, Member; Daowen Zhang, Member; Wenbin Lu, Member; Ratna Sharma, Graduate School Representative
- Semiparametric Estimation and Inference for Censored Regression Models.(2011-11-10) Pang, Lei; Huixia Wang, Co-Chair; Wenbin Lu, Co-Chair; Anastasios Tsiatis, Member; Daowen Zhang, Member; Jerry Davis, Graduate School Representative
- Semiparametric Regression Methods for Longitudinal Data with Informative Observation Times and/or Dropout.(2011-06-16) Cai, Na; Wenbin Lu, Co-Chair; Hao Zhang, Co-Chair; Anastasios Tsiatis, Member; Daowen Zhang, Member; David Baumer, Graduate School Representative
- Solution Paths for the Generalized Lasso with Applications to Spatially Varying Coefficients Regression.(2018-03-29) Zhao, Yaqing; Howard Bondell, Chair; Brian Reich, Member; Wenbin Lu, Member; Daowen Zhang, Member; Joseph Roise, Graduate School Representative
