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Dissertation_XiaoboLiu.pdf (2.67 MB)

ETD Abstract Container

Abstract Header

Optimal Timing to Start Treatment Using Structural Failure Time Models

Liu, Xiaobo
http://orcid.org/0000-0002-6997-9558
http://rave.ohiolink.edu/etdc/view?acc_num=case1532132805119808

Abstract Details

, Doctor of Philosophy, Case Western Reserve University, Epidemiology and Biostatistics.
A structural failure time model is proposed to find the optimal time to start the treatment on a continuous scale in order to maximize the survival. The method is motivated by the need to determine the dialysis initiation time in chronic kidney disease (CKD) patients for maximum survival. The proposed method is based on a g-estimation algorithm and an ignorability assumption. The method is first applied to patients with events, and later extended to include censored observations. The proposed method is evaluated in simulation studies for accuracy of estimation and coverage probability, and applied to the CKD dataset in the case study example.
Jeffrey Albert (Advisor)
Biostatistics

Recommended Citations

Citations

  • Liu, X. (n.d.). Optimal Timing to Start Treatment Using Structural Failure Time Models [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1532132805119808

    APA Style (7th edition)

  • Liu, Xiaobo. Optimal Timing to Start Treatment Using Structural Failure Time Models. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1532132805119808.

    MLA Style (8th edition)

  • Liu, Xiaobo. "Optimal Timing to Start Treatment Using Structural Failure Time Models." Doctoral dissertation, Case Western Reserve University. Accessed APRIL 23, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1532132805119808

    Chicago Manual of Style (17th edition)

case1532132805119808
72
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.