In this work, we provide an overview of the Cox proportional hazards model for time to event or survival analysis and the notion of propensity score matching to deal with confounding factors. A full analysis is reported in Chapter 2 concerning mortality for in-center dialysis patients with sickle cell disease to demonstrate the application of a general analysis strategy that has some logistical benefits over more traditional approaches to accounting for confounding variables. We also provide some insight and discussions on the challenges and future research questions that will emerge when trying to implement this strategy as a monitoring tool over time.
","abstract_html":"<p>In this work, we provide an overview of the Cox proportional hazards model for time to event or survival analysis and the notion of propensity score matching to deal with confounding factors. A full analysis is reported in Chapter 2 concerning mortality for in-center dialysis patients with sickle cell disease to demonstrate the application of a general analysis strategy that has some logistical benefits over more traditional approaches to accounting for confounding variables. We also provide some insight and discussions on the challenges and future research questions that will emerge when trying to implement this strategy as a monitoring tool over time.</p>","abstract_has_math":false,"creators":["Garrison, Adam"],"institution":null,"degree_name":"Master of Science - Statistics","degree_level":"Thesis","degree_discipline":"Mathematics and Statistics","degree_department":null,"school":null,"contributors":["Jacob Turner"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05-08T07:00:00Z","date_published":"2023-05-08T07:00:00Z","updated_at":"2026-07-24T04:30:45Z","subjects":["Propensity Score Matching","Survival Analysis","Applied Statistics","Statistical Methodology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.sfasu.edu/etds/511","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jacob Turner"]},{"key":"dc:creator","label":"Author","values":["Garrison, Adam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-05-08T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics and Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science - Statistics"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Propensity Score Matching","Survival Analysis","Applied Statistics","Statistical Methodology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.sfasu.edu/etds/511"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this work, we provide an overview of the Cox proportional hazards model for time to event or survival analysis and the notion of propensity score matching to deal with confounding factors. A full analysis is reported in Chapter 2 concerning mortality for in-center dialysis patients with sickle cell disease to demonstrate the application of a general analysis strategy that has some logistical benefits over more traditional approaches to accounting for confounding variables. We also provide some insight and discussions on the challenges and future research questions that will emerge when trying to implement this strategy as a monitoring tool over time.
"]},{"key":"dc:title","label":"Title","values":["An Analysis of All-Cause Mortality on Patients with Sickle Cell Disease and Kidney Disease using Propensity Score Matching"]}]}],"canonical_facts":{"dc:contributor":["Jacob Turner"],"dc:creator":["Garrison, Adam"],"dc:date.available":["2023-05-08T07:00:00Z"],"dc:description.abstract":["In this work, we provide an overview of the Cox proportional hazards model for time to event or survival analysis and the notion of propensity score matching to deal with confounding factors. A full analysis is reported in Chapter 2 concerning mortality for in-center dialysis patients with sickle cell disease to demonstrate the application of a general analysis strategy that has some logistical benefits over more traditional approaches to accounting for confounding variables. We also provide some insight and discussions on the challenges and future research questions that will emerge when trying to implement this strategy as a monitoring tool over time.
"],"dc:identifier":["https://scholarworks.sfasu.edu/etds/511"],"dc:subject":["Propensity Score Matching","Survival Analysis","Applied Statistics","Statistical Methodology"],"dc:title":["An Analysis of All-Cause Mortality on Patients with Sickle Cell Disease and Kidney Disease using Propensity Score Matching"],"thesis:degree_discipline":["Mathematics and Statistics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science - Statistics"]},"updated_at":"2026-07-24T04:30:45Z"}