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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…

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…

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…

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…

带序贯转移时间的动态治疗方案的贝叶斯非参数估计AI 解读

We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially…

基于疗效与毒性联合效用的两周期贝叶斯剂量探索AI 解读

Juhee Lee, Peter F. Thall, Yuan Ji & Peter MüllerJuhee Lee, Department of Statistics, Ohio State University, Columbus, OH 43210 (E-mail: ).Peter F. Thall, Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77230-1402 (E-mail: ).Yuan Ji, Center for Clinical and Research Informatics, North Shore University Health System, Evanston, IL 60201 (E-mail: ).Peter Müller, Department of Mathematics, University of Texas, Austin, TX 78712 (E-mail:…

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…

晚期前列腺癌序贯随机试验中可实施动态治疗策略的评估AI 解读

We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients were to be initially randomized among four chemotherapies, and that those who responded poorly were to be rerandomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs' overall success rates, with success defined by the occurrence of…

复发事件分析：随机区间计数数据的非参数方法AI 解读

Many clinical trials require comparison among treatment groups of the rates over time of a particular recurrent event. The event typically reflects disease morbidity rather than mortality. Some examples are episodes of hypoglycemia in diabetics, angina pectoris in patients with heart disease, and seizures in epileptics. Trial protocol often requires that each patient report only the number of events occurring between clinic visits, so the exact times of the successive events…

Huber 意义下尺度参数的稳健 M 估计及指数分布应用AI 解读

Huber's (1964) classical theory of robust M-estimation of a location parameter is also applicable to nonnegative data X having scale parameter θ, since Y = log X has location parameter log θ. In the present article, Huber's location parameter results are reformulated in the scale parameter context and then applied to the problem of robust estimation of the parameter of the exponential distribution. The robust M-estimator is obtained here as the solution of a minimax problem,…

摘要暂缺

随机过程：数学理论综述AI 解读

1. General Introduction.- 2. Second-Order Random Functions.- 3. Stationary Second-Order Processes.- 4. Interpolation and Prediction.- 5. Strictly-Stationary Processes and Ergodic Theory.- 6. Markov Transition Functions.- 7. The Application of Semigroup Theory.- 8. Markov Processes.- 9. Strong Markov Processes.- 10. Martingale Theory.- Appendix 1.- Appendix 2.