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Dissertation Defense: Wei-En Lu

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July 11, 2024
9:00AM - 9:30AM
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2024-07-11 09:00:00 2024-07-11 09:30:00 Dissertation Defense: Wei-En Lu Dissertation Defense: Wei-En Lu, Biostatistics Ph.D. Candidate Title:  Causal Inference in Case-Cohort Studies Using Restricted Mean Survival TimeAbstract: In large observational epidemiological studies with survival outcome and low event rates, the stratified case-cohort design is commonly used to reduce the cost associated with covariate measurement. The goal of many of these studies is to determine whether a cause-and-effect relationship exists between some treatment and an outcome rather an associative relationship. Therefore, a method for estimating the causal effect under the stratified case-cohort design is needed. In this dissertation, we propose to estimate the causal effect of treatment on survival outcome using restricted mean survival time (RMST) difference as the causal effect measure under the stratified case-cohort design and using propensity score stratification or matching to adjust for confounding bias that is present in observational studies.First, we propose a propensity score stratified RMST estimation strategy under the stratified case-cohort design. We established the asymptotic normality of the proposed estimator. Based on the simulation study, the proposed method performs well and is simple to implement in practice. We also applied the proposed method to the Atherosclerosis Risk in Communities (ARIC) Study to estimate the marginal causal effect of high sensitivity C-reactive protein level on coronary heart disease survival.  As an alternative to propensity score stratification, we proposed a propensity score matched RMST estimation strategy under the stratified case-cohort design. The asymptotic normality of the proposed estimator was established and due to the matching design, the correlation that exists within the matched set was accounted for. Simulation studies also demonstrated that the proposed method has adequate performance and outperforms the competing methods. The proposed method was also used to estimate the marginal causal effect of high sensitivity C-reactive protein level on coronary heart disease survival in the ARIC study. Advisor: Dr. Ai NiZoom Link: https://osu.zoom.us/j/99785933827?pwd=Q1wCkztsbdtpZNdn4KS4HbbyTl8b01.1   Zoom Link Below Department of Statistics stat@osu.edu America/New_York public

Dissertation Defense: Wei-En Lu, Biostatistics Ph.D. Candidate

 

Title:  Causal Inference in Case-Cohort Studies Using Restricted Mean Survival Time

Abstract: In large observational epidemiological studies with survival outcome and low event rates, the stratified case-cohort design is commonly used to reduce the cost associated with covariate measurement. The goal of many of these studies is to determine whether a cause-and-effect relationship exists between some treatment and an outcome rather an associative relationship. Therefore, a method for estimating the causal effect under the stratified case-cohort design is needed. In this dissertation, we propose to estimate the causal effect of treatment on survival outcome using restricted mean survival time (RMST) difference as the causal effect measure under the stratified case-cohort design and using propensity score stratification or matching to adjust for confounding bias that is present in observational studies.

First, we propose a propensity score stratified RMST estimation strategy under the stratified case-cohort design. We established the asymptotic normality of the proposed estimator. Based on the simulation study, the proposed method performs well and is simple to implement in practice. We also applied the proposed method to the Atherosclerosis Risk in Communities (ARIC) Study to estimate the marginal causal effect of high sensitivity C-reactive protein level on coronary heart disease survival.

  As an alternative to propensity score stratification, we proposed a propensity score matched RMST estimation strategy under the stratified case-cohort design. The asymptotic normality of the proposed estimator was established and due to the matching design, the correlation that exists within the matched set was accounted for. Simulation studies also demonstrated that the proposed method has adequate performance and outperforms the competing methods. The proposed method was also used to estimate the marginal causal effect of high sensitivity C-reactive protein level on coronary heart disease survival in the ARIC study.

 

Advisor: Dr. Ai Ni

Zoom Link: https://osu.zoom.us/j/99785933827?pwd=Q1wCkztsbdtpZNdn4KS4HbbyTl8b01.1