Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Identification of Causal Effect Modifiers for Time to Relapse and A Weighted Generalized Win Odds Regression Model for Composite Endpoints

Wang, Bang (2023) Identification of Causal Effect Modifiers for Time to Relapse and A Weighted Generalized Win Odds Regression Model for Composite Endpoints. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img] PDF
Restricted to University of Pittsburgh users only until 26 January 2025.

Download (1MB) | Request a Copy


This dissertation addresses the general topic of treatment effect evaluation and comprises two distinct projects.

In project I, we create an intuitive and readily implementable framework to facilitate the discovery of treatment effect modifiers and to make treatment recommendations for time-to-event outcomes. To minimize the impact of a misspecified main effect and avoid complex modeling, we construct the framework by matching the treated with the controls and modeling the conditional average treatment effect via regressing the difference in the observed outcomes of a matched pair on the average moderators. Censoring is handled by the Inverse-probability-of-censoring weighting. After matching, the framework can be flexibly combined with popular variable selection and prediction methods such as linear regression, and LASSO to provide various combinations of potential moderators. The optimal combination is determined by the out-of-bag prediction error and the area under the receiver operating characteristic curve in making correct treatment recommendations. We compare the performance of various combined moderators through simulations and real data analysis. Our approach can be easily implemented using existing R packages, resulting in a straightforward optimal combined moderator to make treatment recommendations.

The time-to-first-event analysis is frequently employed in studies involving multiple event times in which all components are treated equally regardless of their clinical significance. In project II, we focus on Win Odds which can handle different types of outcomes and allow for a hierarchical ordering in component outcomes. A proportional Win Odds regression model is proposed to evaluate the treatment effect on multiple outcomes while controlling for other risk factors. The model is easily interpretable as a standard logistic regression model. However, the proposed Win Odds regression is more advanced; multiple outcomes of different types can be modeled together, and the estimating equation is constructed based on all possible and potentially dependent pairings of a treated individuals and a control ones. In addition, informative ties are carefully distinguished from those inconclusive comparisons due to censoring and the latter is handled via the IPCW. We establish the asymptotic properties of the estimated regression coefficients using the U-statistic theory and demonstrate the finite sample performance through numerical studies.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Wang, Bangbaw90@pitt.edubaw900000-0003-2434-3262
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCheng, Yuyucheng@pitt.eduyucheng
Committee MemberChen, Kehuikhchen@pitt.edukhchen
Committee MemberIyengar, Satishssi@pitt.edussi
Committee MemberJeong, Jong Hjjeong@pitt.edujjeong
Date: 26 January 2023
Date Type: Publication
Defense Date: 2 December 2022
Approval Date: 26 January 2023
Submission Date: 5 December 2022
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 77
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Causal effect modifiers; Matching; IPCW; Composite endpoints; U-statistics-based general estimating equation; Win statistic.
Date Deposited: 26 Jan 2023 15:16
Last Modified: 26 Jan 2023 15:16


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item