Bayesian Methods for Smart Designs with Re-Randomization Based on a Continuous Outcome and Estimating Survival Outcomes when COVID-19 Is a Competing Risk in Cancer Patients
Bayesian Methods for Smart Designs with Re-Randomization Based on a Continuous Outcome and Estimating Survival Outcomes when COVID-19 Is a Competing Risk in Cancer Patients
- 자료유형
- 학위논문파일 국외
- ISBN
- 9798471101289
- DDC
- 574
- 서명/저자
- Bayesian Methods for Smart Designs with Re-Randomization Based on a Continuous Outcome and Estimating Survival Outcomes when COVID-19 Is a Competing Risk in Cancer Patients
- 발행사항
- [Sl] : University of Michigan, 2021
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2021
- 형태사항
- 121 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
- 주기사항
- Advisor: Kidwell, Kelley M.;Schipper, Matthew J.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2021.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 사용제한주기
- This item must not be added to any third party search indexes.
- 일반주제명
- Biostatistics
- 일반주제명
- Biomedical engineering
- 일반주제명
- Oncology
- 일반주제명
- Pathology
- 키워드
- Clinical trial
- 키워드
- COVID-19
- 키워드
- Bayesian
- 키워드
- Small sample
- 기타저자
- University of Michigan Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 83-05B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
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