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BAP：多药物多剂量试验的贝叶斯生物标志物辅助平台设计AI 解读

Assessing the long-term benefits of new treatments can be expensive and time-consuming, particularly in disease areas with unmet medical needs. While platform trials enable the evaluation of multiple interventions simultaneously, they currently cannot assess studies involving multiple agents and doses or utilize longitudinal biomarkers in decision-making. We propose a Bayesian biomarker-assisted platform design that offers a unified framework for evaluating multiple…

具有自适应患者错峰入组规则的贝叶斯Ⅰ—Ⅱ期试验设计AI 解读

For first-in-human dose-finding trials, to protect patient safety, regulatory agencies may enforce strict within-cohort staggering rules that require delaying treatment of each patient in the first cohort at an untried dose until dose-limiting toxicities (DLTs) of all previously treated patients have been evaluated. Consequently, many new patients may face therapy delays, which reduces their probability of achieving a response due to disease progression, or be treated…

利用贝叶斯非参数共同原子回归构建临床试验合成对照AI 解读

The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this paper, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonparametric Bayesian common atoms mixture model that allows us to find equivalent population strata in the EHR and the…

BAGS：具有亚组特异生存比较的贝叶斯自适应群序贯试验设计AI 解读

Ruitao Lin, Peter F. Thall* & Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Peter F. Thall rex@mdanderson.org Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TA 77030.AbstractA Bayesian group sequential design is proposed that performs survival comparisons within patient…

癌种特异性发病年龄外显率的贝叶斯半参数估计：Li-Fraumeni综合征应用AI 解读

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration…

优化动态治疗方案的贝叶斯机器学习方法AI 解读

Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This paper describes a new approach for optimizing dynamic treatment regimes that bridges the gap between Bayesian inference and existing approaches, like Q-learning. The proposed approach fits a series of Bayesian regression models, one…

免疫治疗的贝叶斯 I/II 期临床试验设计AI 解读

Immunotherapy is an innovative treatment approach that stimulates a patient's immune system to fight cancer. It demonstrates characteristics distinct from conventional chemotherapy and stands to revolutionize cancer treatment. We propose a Bayesian phase I/II dosefinding design that incorporates the unique features of immunotherapy by simultaneously considering three outcomes: immune response, toxicity and efficacy. The objective is to identify the biologically optimal dose,…

精准医疗中基于生物标志物的贝叶斯Ⅰ/Ⅱ期剂量探索AI 解读

Beibei Guoa* & Ying Yuanba Department of Experimental Statistics, Louisiana State University, Baton Rouge, LAb Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TXCONTACT Beibei Guo beibeiguo@lsu.edu Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are…

基于半竞争风险效用的非小细胞肺癌稳健治疗比较AI 解读

A design is presented for a randomized clinical trial comparing two second-line treatments, chemotherapy versus chemotherapy plus reirradiation, for treatment of recurrent non-small-cell lung cancer. The central research question is whether the potential efficacy benefit that adding reirradiation to chemotherapy may provide justifies its potential for increasing the risk of toxicity. The design uses two co-primary outcomes: time to disease progression or death, and time to…

用数据增广支持结局延迟的一至二期临床试验AI 解读

A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the…

对称正定矩阵的局部多项式回归AI 解读

Summary Local polynomial regression has received extensive attention for the non-parametric estimation of regression functions when both the response and the covariate are in Euclidean space. However, little has been done when the response is in a Riemannian manifold. We develop an intrinsic local polynomial regression estimate for the analysis of symmetric positive definite matrices as responses that lie in a Riemannian manifold with covariate in Euclidean space. The…

一期临床试验中基于贝叶斯模型平均的持续再评估法AI 解读

The continual reassessment method (CRM) is a popular dose-finding design for phase I clinical trials. This method requires that practitioners prespecify the toxicity probability at each dose. Such prespecification can be arbitrary, and different specifications of toxicity probabilities may lead to very different design properties. To overcome the arbitrariness and further enhance the robustness of the design, we propose using multiple parallel CRM models, each with a…