Browsing by Subject "Optimal biological dose"

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    A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials
    (Wiley, 2017-01-15) Zang, Yong; Lee, J. Jack; Biostatistics, School of Public Health
    We propose a robust two-stage design to identify the optimal biological dose for phase I/II clinical trials evaluating both toxicity and efficacy outcomes. In the first stage of dose finding, we use the Bayesian model averaging continual reassessment method to monitor the toxicity outcomes and adopt an isotonic regression method based on the efficacy outcomes to guide dose escalation. When the first stage ends, we use the Dirichlet-multinomial distribution to jointly model the toxicity and efficacy outcomes and pick the candidate doses based on a three-dimensional volume ratio. The selected candidate doses are then seamlessly advanced to the second stage for dose validation. Both toxicity and efficacy outcomes are continuously monitored so that any overly toxic and/or less efficacious dose can be dropped from the study as the trial continues. When the phase I/II trial ends, we select the optimal biological dose as the dose obtaining the minimal value of the volume ratio within the candidate set. An advantage of the proposed design is that it does not impose a monotonically increasing assumption on the shape of the dose-efficacy curve. We conduct extensive simulation studies to examine the operating characteristics of the proposed design. The simulation results show that the proposed design has desirable operating characteristics across different shapes of the underlying true dose-toxicity and dose-efficacy curves. The software to implement the proposed design is available upon request.
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    Statistical Methods for Cancer Research
    (2024-01) Han, Yan; Zhao, Yi; Tu, Wanzhu; Li, Yang; Zhang, Jianjun
    Phase I/II clinical trial design is pivotal for achieving optimal therapeutic effect in immunotherapy and drug combination therapy for cancer treatment. Additionally, the identification of biomarkers associated with the risk of severe complications during cancer therapy is a crucial research area. This dissertation contains three related topics, which focus on adaptive Phase I/II clinical trial design and the identification of biomarkers relevant to cancer research. The first topic focuses on developing a two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD through a utility function. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. The second topic focuses on dose optimization in drug-combination trials. We propose the Great Wall design, which employs a "divide-and-conquer" algorithm to address the issue of partial order of toxicity. It constructs a candidate set of the most promising dose combinations using the mean utility method. The patients assigned to the candidate set are followed to collect the survival outcomes and the final optimal dose combination is then select to maximize the survival benefit. A simulation study confirmed the desirable operating characteristics of the Great Wall design, compared with other conventional phase I/II designs for drug-combination trials. The last topic of my dissertation is prospective assessment of risk biomarkers of sinusoidal obstruction syndrome (SOS) after hematopoietic cell transplantation (HCT). We aimed to define risk groups for SOS occurrence using three proteins: L-Ficolin, Hyaluronic Acid (HA), and Stimulation-2 (ST2), by assessing SOS incidence at day 35 post-HCT, and overall survival (OS) at day 100 post-HCT. We conclude that L-Ficolin, HA, and ST2 levels measured as early as three days post-HCT improved risk stratification for SOS occurrence and OS.