The landscape of drug development in oncology has changed from conventional chemotherapies to molecular targeted therapies and immunotherapies, which provide innovative therapeutic modalities for treating cancers. These novel therapeutic agents work through mechanisms that fundamentally differ from standard chemotherapeutic agents, making the conventional trial design paradigm inefficient and dysfunctional. Specifically, the focus of dose-finding trials has shifted from finding the maximum tolerated dose (MTD) to the optimal biological dose (OBD), defined as the dose that optimizes the risk–benefit tradeoff. How to accurately identify the OBD and its dosing schedule is of great importance to maximize efficacy and safety of targeted therapies and immunotherapies. The US Food and Drug Administration (FDA) Oncology Center of Excellence recently launched Project Optimus to accelerate this paradigm shift. In addition, once the OBD and recommended phase 2 dose (PR2D) are determined, how to effectively monitor short-term and long-term efficacy in phase II trials, in particular basket trials, is critical for the development of targeted therapies and immunotherapies.
In this dissertation, we propose Bayesian adaptive clinical trial designs to address these challenges. Specifically, we propose (a) a novel Bayesian dose-finding design to find the OBD of drug combination based on risk-benefit tradeoff, (b) a Bayesian adaptive design that simultaneously optimizes dose and schedule based on efficacy, toxicity, and PK data, and (c) a phase II basket trial design that uses Bayesian hierarchical model to borrow information across treatment arms for efficient termination of ineffective treatment arms based on short-term and long-term endpoints. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed designs. Results show that the proposed designs outperform existing approaches and provide robust and efficient tools to accelerate the development of targeted therapies and immunotherapies.
","abstract_html":"<p>The landscape of drug development in oncology has changed from conventional chemotherapies to molecular targeted therapies and immunotherapies, which provide innovative therapeutic modalities for treating cancers. These novel therapeutic agents work through mechanisms that fundamentally differ from standard chemotherapeutic agents, making the conventional trial design paradigm inefficient and dysfunctional. Specifically, the focus of dose-finding trials has shifted from finding the maximum tolerated dose (MTD) to the optimal biological dose (OBD), defined as the dose that optimizes the risk–benefit tradeoff. How to accurately identify the OBD and its dosing schedule is of great importance to maximize efficacy and safety of targeted therapies and immunotherapies. The US Food and Drug Administration (FDA) Oncology Center of Excellence recently launched Project Optimus to accelerate this paradigm shift. In addition, once the OBD and recommended phase 2 dose (PR2D) are determined, how to effectively monitor short-term and long-term efficacy in phase II trials, in particular basket trials, is critical for the development of targeted therapies and immunotherapies.</p> <p>In this dissertation, we propose Bayesian adaptive clinical trial designs to address these challenges. Specifically, we propose (a) a novel Bayesian dose-finding design to find the OBD of drug combination based on risk-benefit tradeoff, (b) a Bayesian adaptive design that simultaneously optimizes dose and schedule based on efficacy, toxicity, and PK data, and (c) a phase II basket trial design that uses Bayesian hierarchical model to borrow information across treatment arms for efficient termination of ineffective treatment arms based on short-term and long-term endpoints. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed designs. Results show that the proposed designs outperform existing approaches and provide robust and efficient tools to accelerate the development of targeted therapies and immunotherapies.</p>","abstract_has_math":false,"creators":["Lu, Mengyi"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation (PhD)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ying Yuan, Ph.D.","Yisheng Li, Ph.D.","Suyu Liu, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12-01T08:00:00Z","date_published":"2022-12-01T08:00:00Z","updated_at":"2026-07-24T05:48:47Z","subjects":["Early phase clinical trial design","dose-finding","dose-schedule finding","monitoring rules","Biostatistics","Clinical Trials"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1229","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ying Yuan, Ph.D.","Yisheng Li, Ph.D.","Suyu Liu, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Lu, Mengyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-07T08:00:00Z"]},{"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":["Early phase clinical trial design","dose-finding","dose-schedule finding","monitoring rules","Biostatistics","Clinical Trials"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1229"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The landscape of drug development in oncology has changed from conventional chemotherapies to molecular targeted therapies and immunotherapies, which provide innovative therapeutic modalities for treating cancers. These novel therapeutic agents work through mechanisms that fundamentally differ from standard chemotherapeutic agents, making the conventional trial design paradigm inefficient and dysfunctional. Specifically, the focus of dose-finding trials has shifted from finding the maximum tolerated dose (MTD) to the optimal biological dose (OBD), defined as the dose that optimizes the risk–benefit tradeoff. How to accurately identify the OBD and its dosing schedule is of great importance to maximize efficacy and safety of targeted therapies and immunotherapies. The US Food and Drug Administration (FDA) Oncology Center of Excellence recently launched Project Optimus to accelerate this paradigm shift. In addition, once the OBD and recommended phase 2 dose (PR2D) are determined, how to effectively monitor short-term and long-term efficacy in phase II trials, in particular basket trials, is critical for the development of targeted therapies and immunotherapies.
In this dissertation, we propose Bayesian adaptive clinical trial designs to address these challenges. Specifically, we propose (a) a novel Bayesian dose-finding design to find the OBD of drug combination based on risk-benefit tradeoff, (b) a Bayesian adaptive design that simultaneously optimizes dose and schedule based on efficacy, toxicity, and PK data, and (c) a phase II basket trial design that uses Bayesian hierarchical model to borrow information across treatment arms for efficient termination of ineffective treatment arms based on short-term and long-term endpoints. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed designs. Results show that the proposed designs outperform existing approaches and provide robust and efficient tools to accelerate the development of targeted therapies and immunotherapies.
"]},{"key":"dc:title","label":"Title","values":["Bayesian Adaptive Clinical Trial Design"]}]}],"canonical_facts":{"dc:contributor":["Ying Yuan, Ph.D.","Yisheng Li, Ph.D.","Suyu Liu, Ph.D."],"dc:creator":["Lu, Mengyi"],"dc:date.available":["2023-12-07T08:00:00Z"],"dc:description.abstract":["The landscape of drug development in oncology has changed from conventional chemotherapies to molecular targeted therapies and immunotherapies, which provide innovative therapeutic modalities for treating cancers. These novel therapeutic agents work through mechanisms that fundamentally differ from standard chemotherapeutic agents, making the conventional trial design paradigm inefficient and dysfunctional. Specifically, the focus of dose-finding trials has shifted from finding the maximum tolerated dose (MTD) to the optimal biological dose (OBD), defined as the dose that optimizes the risk–benefit tradeoff. How to accurately identify the OBD and its dosing schedule is of great importance to maximize efficacy and safety of targeted therapies and immunotherapies. The US Food and Drug Administration (FDA) Oncology Center of Excellence recently launched Project Optimus to accelerate this paradigm shift. In addition, once the OBD and recommended phase 2 dose (PR2D) are determined, how to effectively monitor short-term and long-term efficacy in phase II trials, in particular basket trials, is critical for the development of targeted therapies and immunotherapies.
In this dissertation, we propose Bayesian adaptive clinical trial designs to address these challenges. Specifically, we propose (a) a novel Bayesian dose-finding design to find the OBD of drug combination based on risk-benefit tradeoff, (b) a Bayesian adaptive design that simultaneously optimizes dose and schedule based on efficacy, toxicity, and PK data, and (c) a phase II basket trial design that uses Bayesian hierarchical model to borrow information across treatment arms for efficient termination of ineffective treatment arms based on short-term and long-term endpoints. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed designs. Results show that the proposed designs outperform existing approaches and provide robust and efficient tools to accelerate the development of targeted therapies and immunotherapies.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1229"],"dc:subject":["Early phase clinical trial design","dose-finding","dose-schedule finding","monitoring rules","Biostatistics","Clinical Trials"],"dc:title":["Bayesian Adaptive Clinical Trial Design"],"thesis:degree_level":["Dissertation (PhD)"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T05:48:47Z"}