Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Epidemiology and Biostatistics

Supervisor

Bauer Greta R

Affiliation

Eli Coleman Institute for Sexual and Gender Health, University of Minnesota

2nd Supervisor

Lizotte Daniel J

Abstract

Background and objectives: Health disparities in breast cancer contribute to needlessly poor health outcomes. This research aimed to 1) synthesize the evidence on variations in time to breast cancer treatment initiation and survival across racial/ethnic groups using a DAG-based review method and 2) to investigate challenges and mitigation strategies stemming from sparse-data bias in intersectional analyses relevant to breast cancer.

Methods: To achieve objective 1: A comprehensive systematic search was performed to describe implicit causal relationships between racial/ethnic group membership and time to treatment initiation and overall survival outcomes. For objective 2, a simulation study using mediation analysis with an accelerated failure time model was conducted to investigate the challenges that sparse data may present when estimating the causal effect of intersectional categories on survival.

Results: In the systematic review, forty studies on cohorts of female patients diagnosed with stage I-III breast cancer were included. Studies reported significant disparities in delays in time to first treatment and subsequent impact on survival. The results of the simulation study demonstrated how sparse data bias can affect mediation effect measures. The scenarios with varying numbers of events per variables and cross-stratified groups showed that, despite a large sample size, challenges related to sparsity persist, often leading to unstable estimates across different scenarios.

Conclusions: Persistent disparities in breast cancer treatment and mortality among racial/ethnic groups, especially between White and Black women, emphasize the need for equitable care and the elimination of these disparities to improve survival across diverse populations.

Summary for Lay Audience

This thesis explores racial disparities in breast cancer and conceptualizes causal mediation analysis of time-to-event data within an intersectionality framework. The primary aim of this thesis was to shift the focus from mere association to causation in research on racial disparities. Our first study aimed to develop and refine a method for systematically reviewing the evidence on ethnoracial disparities in the initiation of breast cancer treatments and their impact on patient survival in the United States.

Following the protocol paper, our second study reviewed the literature concerning racial disparities, time to treatment initiation, and survival among breast cancer patients. We applied a recently developed method for evidence assessment using causal diagrams, specifically directed acyclic graphs (DAGs), to consolidate and clarify the causal mechanisms linking both measured and unmeasured factors identified in the studies. A significant outcome of our review was the presentation of a composite causal DAG model recommended for future causal research into ethnoracial disparities in breast cancer treatment timing. We believe this review provides valuable insights and contributes meaningfully to the existing literature, offering a robust model for subsequent research in this critical area.

Our third study investigated the challenge of sparsity bias in assessing breast cancer disparities through an intersectional lens when modeling cross-stratified intersectional categories of social determinants. In this simulation study, we proposed the mediation analysis with an accelerated failure time model as a robust survival method. This study explored the causal interpretation of race using different dimensions of cross-coded categories, focusing on the comparable reference group, and suggested an optimal sample size for future cohort studies in this context.

In summary, this thesis combines all studies with the overarching goal of addressing causal mechanisms rather than associations, potentially enhancing current policies in breast cancer health equity.

Download

Share

COinS