Novel Statistical Methods for Composite Endpoints- [electronic resource]
Novel Statistical Methods for Composite Endpoints- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101239
- ISBN
- 9798379707101
- DDC
- 574
- 저자명
- Wang, Tuo.
- 서명/저자
- Novel Statistical Methods for Composite Endpoints - [electronic resource]
- 발행사항
- [S.l.]: : The University of Wisconsin - Madison., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(135 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Mao, Lu.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The recent Influenza Vaccine to Effectively Stop cardio Thoracic Events and Decompensated heart failure (INVESTED) trial compares the effects of two influenza vaccines in high-risk cardiovascular patients on the composite of all-cause death and recurrent cardiopulmonary hospitalizations. The INVESTED trial has two major challenges that cannot be adequately addressed by conventional statistical methodologies. 1) The INVESTED trial is a multi-season clinical trial with a randomize-once strategy. Patients enrolled from previous seasons who stay alive and remain in the study will be treated according to the initial randomization in subsequent seasons. The randomize-once strategy may induce selective attrition from earlier seasons for the non-randomized cohorts. 2) Multivariate prioritized endpoints are used for the primary analysis. The traditional approaches to analyze composite endpoints, such as the Kaplan-Meier estimator and Cox proportional hazards regression, focus on the time-to-first event and thus ignore the hierarchical structure among the components. In this dissertation, we aim to address these challenges.In the first project, to address the potentially selective attrition from earlier seasons for the non-randomized cohorts, we develop an inverse probability of treatment weighting method using season-specific propensity scores to produce unbiased estimates of survival functions or hazard ratios. As for the inference procedure, bootstrap variance estimators are used to account for the randomness in the estimated weights and the potential correlations in repeated events within each patient from season to season.The subsequent three projects focus on developing novel approaches to analyze composite endpoints that can properly prioritize different components of the composite endpoints based on their clinical importance. The second and third project focus on the win ratio statistics, as introduced in Pocock et al. (2012), while the fourth project extends the survival tree methodology to prioritized composite endpoints.In the second project, we propose the restricted time win ratio to address the major limitation of the traditionally used win ratio, which is heavily influenced by the censoring distribution. By calculating the win ratio at a pre-specified time point and estimating it using an imputation algorithm based on fully conditional specification algorithm, this method offers an unbiased and robust estimation of the treatment effect while accounting for the hierarchical structure of the composite endpoint.In the third project, we develop a stratified proportional win-fractions (PW) model, extending the win ratio from a two-sample comparison to regression. Under the proportionality assumption, the PW model ensures the covariate-specific win ratios remain invariant to the follow-up times. To address the violation of the proportionality assumption in practice, we develop a stratified PW model that adjusts for certain prognostic factors without setting them as covariates, thus bypassing the proportionality requirement. By allowing for stratification, this method provides robust and efficient estimation of the treatment effect and reduces potential biases arising from the violation of the proportionality assumption.Finally, the fourth project extends the survival tree methodology to handle prioritized composite endpoints by generalizing the twoing criteria and develops a generalized concordance index (C-index) for measuring model performance. This approach offers a nonparametric alternative for analyzing composite endpoints, accommodating complex interactions between covariates and multiple event types. By incorporating the hierarchical nature of composite endpoints into the tree construction process, this method provides a more accurate and interpretable partition of patients into distinct risk groups.
- 일반주제명
- Biostatistics.
- 일반주제명
- Statistics.
- 일반주제명
- Biomedical engineering.
- 키워드
- Estimand
- 키워드
- Propensity score
- 키워드
- Survival tree
- 키워드
- Win ratio
- 기타저자
- The University of Wisconsin - Madison Statistics
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
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