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Inference on win ratio for clustered semi-competing risk data

Zhang, Di (2019) Inference on win ratio for clustered semi-competing risk data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Composite endpoints are commonly used in public health with an anticipation that clinically relevant endpoints as a whole would yield meaningful treatment benefits. The traditional way to analyze composite endpoints leads to difficulties in interpreting study results when the components have different clinical importance. The win ratio statistic was proposed recently to prioritize the important endpoints through sequential comparisons. The statistical method developments for the win ratio were only in randomized controlled trial settings with independent subjects and no potential confounders. We developed statistical frameworks of the win ratio in cluster randomized trial and observational study settings. We focus on composite endpoints of semi-competing risk structure and two arm comparisons.
Firstly, we propose to model the win ratio of cluster-randomized data non-parametrically using bivariate clustered U-statistics. We account for the potential dependence among subjects within the same cluster. The asymptotic joint distribution of the joint clustered U-statistics and the asymptotic covariance are derived. Then the proposed method is illustrated using a multi-center breast cancer clinical trial.
Secondly, the causal inference for the win ratio in observational studies with independent subjects is developed. We propose to use a combination of propensity score analysis with inverse probability weights and U-statistics. The causal estimand of the proposed estimator is average superiority effect, which is based on the average over marginal distributions of potential outcomes for comparison groups. The asymptotic properties of the proposed test statistic and the asymptotic variance are studied.
Lastly, based on the causal inference procedure developed in the second part, we propose a weighted stratified win ratio estimator based on calibrated weights for cluster-correlated data from observational studies. The calibration technique used in the weight estimation creates a good balance of covariates and cluster effects between arms. Additionally, it is robust against misspecified distributional assumptions. The proposed method is applied to an observational study on children with traumatic brain injury, using sites or regions as clusters.
PUBLIC HEALTH SIGNIFICANCE: Our work has important implications to public health, providing new analytical tools to assess the intervention benefits using informative endpoints, to promote public health and transform health care.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Zhang, Didiz11@pitt.edudiz11
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairJeong, Jong H.jjeong@pitt.edujjeong
Committee MemberKang, Chaeryoncrkang@pitt.educrkang
Committee MemberDing, YingYINGDING@pitt.eduyingding
Committee MemberWisniewski, Stephen R.wisniew@edc.pitt.eduwisniew
Date: 27 June 2019
Date Type: Publication
Defense Date: 11 April 2019
Approval Date: 27 June 2019
Submission Date: 3 April 2019
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 79
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Balance of Covariates, Calibration, Causal Inference, Cluster Randomization, Clustered Data, Composite Endpoints, Inverse Probability Weight, Observational Study, Propensity Score, U-Statistic
Date Deposited: 27 Jun 2019 22:02
Last Modified: 01 May 2022 05:15


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