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Bayesian Inference for Treatment Effect

Liu, Jinzhong
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964

Abstract Details

2017, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Evaluation of overall treatment effect and heterogeneity in treatment effect is of interest in both randomized clinical trials and in observational studies. In this thesis, we first develop a Bayesian approach to subgroup analysis using ANOVA models with multiple covariates. We assume a two-arm clinical trial with normally distributed response variable. The covariates are assumed categorical and a priori specified. The subgroups of interest are represented by a collection of models. And we use a model selection approach to find subgroups with heterogeneous effects. Then we propose a Bayesian semiparametric approach for estimating the population mean treatment effect with observational data using Gaussian process (GP), which accomplishes matching and modeling outcome mechanism in a single step. We demonstrate a close link between matching method and GP regression for estimating average treatment effect. The proposed method utilizes a distance similar to Mahalanobis distance but determines the range of matching automatically without imposing a caliper arbitrarily. Finally, we proposed a Bayesian semiparametric approach for predicting the heterogeneous treatment effect for new patients using two conditionally independent Gaussian processes (GP), one for response surface of control group, the other for treatment effect. The prediction can be used to visualize the treatment effect and help researchers investigate the pattern of the treatment effect for different patient baseline characteristics and hence decide whether the treatment is effective for patients with certain characteristics and possibly define a subgroup that the treatment is significantly effective on. We also illustrate the proposed methods using real data obtained from different studies.
Siva Sivaganesan, Ph.D. (Committee Chair)
Bin Huang (Committee Member)
Seongho Song, Ph.D. (Committee Member)
Xia Wang, Ph.D. (Committee Member)
122 p.
Statistics
Bayesian; Subgroup analysis; Causal inference; Gaussian process; Selection bias

Recommended Citations

Citations

  • Liu, J. (2017). Bayesian Inference for Treatment Effect [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964

    APA Style (7th edition)

  • Liu, Jinzhong. Bayesian Inference for Treatment Effect. 2017. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964.

    MLA Style (8th edition)

  • Liu, Jinzhong. "Bayesian Inference for Treatment Effect." Doctoral dissertation, University of Cincinnati, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964

    Chicago Manual of Style (17th edition)

ucin1504803668961964
222
© 2017, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.