The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However, as cancer therapies have evolved from chemotherapy to targeted therapies, these traditional methods have become problematic. Many targeted therapies rarely produce DLT and are administered over multiple cycles, potentially resulting in the accumulation of lower-grade toxicities, which can lead to intolerance, such as dose reduction or interruption. To address this issue, we proposed dual-criterion designs that find the MTD based on both DLT and non-DLT-caused intolerance. These include a model-based design and model-assisted design that allow real-time decision making in the presence of pending data due to long event assessment windows. We then extend this dual-criterion approach by incorporating efficacy to determine the optimal biological dose under a model-assisted design. Compared to DLT-based methods, our approaches exhibit superior operating characteristics when intolerance is the primary driver for determining the MTD and comparable operating characteristics when DLT is the primary driver.
","abstract_html":"<p>The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However, as cancer therapies have evolved from chemotherapy to targeted therapies, these traditional methods have become problematic. Many targeted therapies rarely produce DLT and are administered over multiple cycles, potentially resulting in the accumulation of lower-grade toxicities, which can lead to intolerance, such as dose reduction or interruption. To address this issue, we proposed dual-criterion designs that find the MTD based on both DLT and non-DLT-caused intolerance. These include a model-based design and model-assisted design that allow real-time decision making in the presence of pending data due to long event assessment windows. We then extend this dual-criterion approach by incorporating efficacy to determine the optimal biological dose under a model-assisted design. Compared to DLT-based methods, our approaches exhibit superior operating characteristics when intolerance is the primary driver for determining the MTD and comparable operating characteristics when DLT is the primary driver.</p>","abstract_has_math":false,"creators":["Yang, Yunlong"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation (PhD)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Yuan Ying","Liang Li","J Jack Lee"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-01T07:00:00Z","date_published":"2025-05-01T07:00:00Z","updated_at":"2026-07-24T05:49:08Z","subjects":["Bayesian; Clinical Trial Design; Dose Finding","Biostatistics","Clinical Trials"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1412","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yuan Ying","Liang Li","J Jack Lee"]},{"key":"dc:creator","label":"Author","values":["Yang, Yunlong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-02-20T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation (PhD)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bayesian; Clinical Trial Design; Dose Finding","Biostatistics","Clinical Trials"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1412"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However, as cancer therapies have evolved from chemotherapy to targeted therapies, these traditional methods have become problematic. Many targeted therapies rarely produce DLT and are administered over multiple cycles, potentially resulting in the accumulation of lower-grade toxicities, which can lead to intolerance, such as dose reduction or interruption. To address this issue, we proposed dual-criterion designs that find the MTD based on both DLT and non-DLT-caused intolerance. These include a model-based design and model-assisted design that allow real-time decision making in the presence of pending data due to long event assessment windows. We then extend this dual-criterion approach by incorporating efficacy to determine the optimal biological dose under a model-assisted design. Compared to DLT-based methods, our approaches exhibit superior operating characteristics when intolerance is the primary driver for determining the MTD and comparable operating characteristics when DLT is the primary driver.
"]},{"key":"dc:title","label":"Title","values":["Dual-Criterion Dose Finding Designs for Phase I Clinical Trials"]}]}],"canonical_facts":{"dc:contributor":["Yuan Ying","Liang Li","J Jack Lee"],"dc:creator":["Yang, Yunlong"],"dc:date.available":["2025-02-20T08:00:00Z"],"dc:description.abstract":["The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However, as cancer therapies have evolved from chemotherapy to targeted therapies, these traditional methods have become problematic. Many targeted therapies rarely produce DLT and are administered over multiple cycles, potentially resulting in the accumulation of lower-grade toxicities, which can lead to intolerance, such as dose reduction or interruption. To address this issue, we proposed dual-criterion designs that find the MTD based on both DLT and non-DLT-caused intolerance. These include a model-based design and model-assisted design that allow real-time decision making in the presence of pending data due to long event assessment windows. We then extend this dual-criterion approach by incorporating efficacy to determine the optimal biological dose under a model-assisted design. Compared to DLT-based methods, our approaches exhibit superior operating characteristics when intolerance is the primary driver for determining the MTD and comparable operating characteristics when DLT is the primary driver.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1412"],"dc:subject":["Bayesian; Clinical Trial Design; Dose Finding","Biostatistics","Clinical Trials"],"dc:title":["Dual-Criterion Dose Finding Designs for Phase I Clinical Trials"],"thesis:degree_level":["Dissertation (PhD)"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T05:49:08Z"}