Survival analysis is a critical statistical method in healthcare to assess patient treatment effects and disease progression. Another critical area of statistical methodology in health care is the practice of adaptive designs. Adaptive designs allow for interim analyses to take place during a study and various decisions and actions can take place more ethically. This is beneficial for studies that take multiple years to complete and allows administrators and healthcare providers to make sound decisions as early as possible. A challenging aspect of adaptive designs is that the number of interim analyses is known in advance which is applicable in controlled experiments such as randomized clinical trials.
Motivated and highlighted by our collaborations with Fresenius Medical Care, many clinical studies are observational in nature and have no clear endpoint, making it difficult to determine the number of interim analyses that will be conducted. This research considers the application of survival analysis using adaptive designs within observational studies. To do so, we developed a collection of statistical programs to simulate these types of interim analyses while accounting for the additional complexity that survival data exhibits. Simulations summaries were performed and we will summarize some of the key results including investigations of statistical power, Type-I error control, and parameter estimation performance. Additionally, this work aims to assess the necessary conditions to achieve reasonable power at early looks and/or establish general rules of thumb when designing the study.
","abstract_html":"<p>Survival analysis is a critical statistical method in healthcare to assess patient treatment effects and disease progression. Another critical area of statistical methodology in health care is the practice of adaptive designs. Adaptive designs allow for interim analyses to take place during a study and various decisions and actions can take place more ethically. This is beneficial for studies that take multiple years to complete and allows administrators and healthcare providers to make sound decisions as early as possible. A challenging aspect of adaptive designs is that the number of interim analyses is known in advance which is applicable in controlled experiments such as randomized clinical trials.</p> <p>Motivated and highlighted by our collaborations with Fresenius Medical Care, many clinical studies are observational in nature and have no clear endpoint, making it difficult to determine the number of interim analyses that will be conducted. This research considers the application of survival analysis using adaptive designs within observational studies. To do so, we developed a collection of statistical programs to simulate these types of interim analyses while accounting for the additional complexity that survival data exhibits. Simulations summaries were performed and we will summarize some of the key results including investigations of statistical power, Type-I error control, and parameter estimation performance. Additionally, this work aims to assess the necessary conditions to achieve reasonable power at early looks and/or establish general rules of thumb when designing the study.</p>","abstract_has_math":false,"creators":["Eloise, Quentin"],"institution":null,"degree_name":"Master of Science - Mathematical Sciences","degree_level":"Thesis","degree_discipline":"Mathematics and Statistics","degree_department":null,"school":null,"contributors":["Jacob Turner","Derek Blankenship","Robert Henderson"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-08-09T07:00:00Z","date_published":"2024-08-09T07:00:00Z","updated_at":"2026-07-24T04:30:45Z","subjects":["Survival Analysis","Statistics","Biostatistics","Simulation","Adaptive Designs","Applied Statistics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.sfasu.edu/etds/566","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jacob Turner","Derek Blankenship","Robert Henderson"]},{"key":"dc:creator","label":"Author","values":["Eloise, Quentin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-08-08T07:00:00Z"]},{"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 - Mathematical Sciences"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Survival Analysis","Statistics","Biostatistics","Simulation","Adaptive Designs","Applied Statistics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.sfasu.edu/etds/566"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Survival analysis is a critical statistical method in healthcare to assess patient treatment effects and disease progression. Another critical area of statistical methodology in health care is the practice of adaptive designs. Adaptive designs allow for interim analyses to take place during a study and various decisions and actions can take place more ethically. This is beneficial for studies that take multiple years to complete and allows administrators and healthcare providers to make sound decisions as early as possible. A challenging aspect of adaptive designs is that the number of interim analyses is known in advance which is applicable in controlled experiments such as randomized clinical trials.
Motivated and highlighted by our collaborations with Fresenius Medical Care, many clinical studies are observational in nature and have no clear endpoint, making it difficult to determine the number of interim analyses that will be conducted. This research considers the application of survival analysis using adaptive designs within observational studies. To do so, we developed a collection of statistical programs to simulate these types of interim analyses while accounting for the additional complexity that survival data exhibits. Simulations summaries were performed and we will summarize some of the key results including investigations of statistical power, Type-I error control, and parameter estimation performance. Additionally, this work aims to assess the necessary conditions to achieve reasonable power at early looks and/or establish general rules of thumb when designing the study.
"]},{"key":"dc:title","label":"Title","values":["The Impact of “Multiple Looks” when Performing Survival Analysis"]}]}],"canonical_facts":{"dc:contributor":["Jacob Turner","Derek Blankenship","Robert Henderson"],"dc:creator":["Eloise, Quentin"],"dc:date.available":["2024-08-08T07:00:00Z"],"dc:description.abstract":["Survival analysis is a critical statistical method in healthcare to assess patient treatment effects and disease progression. Another critical area of statistical methodology in health care is the practice of adaptive designs. Adaptive designs allow for interim analyses to take place during a study and various decisions and actions can take place more ethically. This is beneficial for studies that take multiple years to complete and allows administrators and healthcare providers to make sound decisions as early as possible. A challenging aspect of adaptive designs is that the number of interim analyses is known in advance which is applicable in controlled experiments such as randomized clinical trials.
Motivated and highlighted by our collaborations with Fresenius Medical Care, many clinical studies are observational in nature and have no clear endpoint, making it difficult to determine the number of interim analyses that will be conducted. This research considers the application of survival analysis using adaptive designs within observational studies. To do so, we developed a collection of statistical programs to simulate these types of interim analyses while accounting for the additional complexity that survival data exhibits. Simulations summaries were performed and we will summarize some of the key results including investigations of statistical power, Type-I error control, and parameter estimation performance. Additionally, this work aims to assess the necessary conditions to achieve reasonable power at early looks and/or establish general rules of thumb when designing the study.
"],"dc:identifier":["https://scholarworks.sfasu.edu/etds/566"],"dc:subject":["Survival Analysis","Statistics","Biostatistics","Simulation","Adaptive Designs","Applied Statistics"],"dc:title":["The Impact of “Multiple Looks” when Performing Survival Analysis"],"thesis:degree_discipline":["Mathematics and Statistics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science - Mathematical Sciences"]},"updated_at":"2026-07-24T04:30:45Z"}