Statistical Methods for Complex Cancer Trials

复杂癌症试验的统计方法

• 批准号: 8575378
• 负责人: PETER F THALL
• 金额: $ 15.52万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-03 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8575378
• 关键词: Accounting; Adverse event; Algorithms; Bayesian Method; Behavior; Blood; Blood Pressure; Breathing; Characteristics; Clinical; Clinical Trials; Clinical Trials Design; Complex; Computer Simulation; Computer software; Crying; Data; Decision Making; Development; Dose; Drops; Enrollment; Ensure; Ethics; Evaluation; Event; Facial Expression; Family; Future; Goals; Heart Rate; Hybrids; Infant; Infant respiratory distress; Inferior; Intubation; Joints; Learning; Limb structure; Logistics; Lung; Malignant Neoplasms; Medical; Methods; Modeling; Oncologist; Outcome; Oxygen; Patients; Phase; Physicians; Premature Infant; Probability; Procedures; Randomized; Recording of previous events; Relative (related person); Research; Risk; Safety; Sample Size; Schedule; Sedation procedure; Shapes; Source; Speed; Staging; Statistical Methods; Stem cell transplant; Structure; Testing; Therapeutic; Time; Toxic effect; Trachea; Treatment Protocols; Weight; base; cancer therapy; computer program; conventional therapy; design; disability; hemodynamics; improved; public health relevance; respiratory distress syndrome; response; sedative; simulation; surfactant; tool; two-dimensional; user-friendly

DESCRIPTION (provided by applicant): The primary objective of this proposed research is to provide practical models and methods for the design, conduct, and analysis of complex clinical trials not accommodated by conventional trial designs. These are trials of experimental treatment regimens for which little is known about the effects of dose, dose-schedule combination, or dose sequence in multiple cycles, or about short-term or long-term adverse events (toxicities) or efficacy. All models and methods are Bayesian, which facilitates adaptive decision making and accounts for multiple sources of variability. A major difficulty is the inheren tension between the physician's desire to treat the patients enrolled in a trial ethically and the scientific goal to obtain information useful for treatment regime evaluation and refinement to benefit future patients. Consequently, to ensure ethical trials, the designs include sequential, outcome-adaptive "learn-as-you go" decision rules. In the settings considered here, this is complicated by the complexity of patient outcomes, which include both efficacy and toxicity variables, each possibly binary, ordinal, or continuous with possible interval censoring, in some cases observed over multiple treatment cycles. For most of the designs and methods, the desirability of each combination of possible outcomes is quantified by elicited numerical utilities which are used as the basis for adaptive decision-making. This formalizes the idea, inherent in virtually all medical decision-making, of trade-offs between desirable and undesirable therapeutic outcomes, and it provides a method for reducing multiple outcomes to a single decision criterion, the posterior mean utility of each treatment regime, quantifying the relative importance of the outcomes. Methods will be developed for eliciting numerical utilities and establishing utility functions, in the cases of bivariate or trivariate discrete outcomes, and for bivariate event times. Specific Aim 3 is more general in that it accommodates any optimality criterion, including posterior mean utility, efficacy-toxicity probability trade-off, or the continal reassessment method criterion. In some trials with adaptive treatment assignment, the "exploration versus exploitation" problem may occur, wherein a \greedy" algorithm that always chooses the empirically optimal regime may get stuck at a regime that actually is inferior. To deal with this, we will explore hybrid algorithms that include adaptive randomization to reduce the risk of choosing inferior regimes. Computer simulation will be used as a design tool to calibrate design parameters and prior parameters to obtain designs with good frequentist operating characteristics. The Specific Aims are to develop (1) a phase I-II design to optimize (schedule, dose) regimes based on the joint utility of the times to response and toxicity, (2) a family of phase I-II designs, based on bivariate binary or bivariate ordinal (toxicity, efficacy), o optimize the dose sequence of an experimental agent given sequentially in multiple cycles, (3) an algorithm to improve the logistics of outcome-adaptive clinical trials by future data weighted randomization, and (4) phase I-II design, based on three outcomes, to optimize the sedation dose for preterm infants being intubated to treat respiratory distress syndrome. Efficient, user-friendly, freely available computer programs will be developed to facilitate widespread application.

描述（由申请人提供）：这项拟议研究的主要目的是提供实用的模型和方法，以设计，进行和分析的复杂临床试验，而不受常规试验设计的适应。这些是实验治疗方案的试验，对于多个循环中的剂量，剂量 - 安排组合或剂量序列的影响，或短期或长期不良事件（毒性）或疗效几乎没有任何了解。所有模型和方法都是贝叶斯，它有助于自适应决策，并解释了多种可变性来源。一个主要的困难是医生希望以道德进行试验的患者的愿望与获得治疗方案评估有用的信息的科学目标之间的固有张力，以使未来的患者受益。因此，为了确保道德试验，设计包括顺序，自适应的“学习”决策规则。在此处考虑的设置中，这对于患者结局的复杂性（包括疗效和毒性变量）的复杂性很复杂，这些变量可能是二进制，序数或连续的，在某些情况下可能会在多个治疗周期观察到。对于大多数设计和方法，可能结果的每种组合的可取性都是通过引起的数值实用程序来量化的，这些实用程序被用作自适应决策的基础。这正式化了几乎所有医疗决策，理想和不良治疗结果之间权衡的固有的想法，并且提供了一种将多重结果减少到单个决策标准的方法，即每个治疗方案的后平均值效用，从而量化了相对重要性。将开发用于启发数值实用程序和建立效用函数的方法，在双变量或三转离心结果以及双变量事件时间的情况下。具体目标3更为普遍，因为它满足了任何最佳标准，包括后平均效用，有效性毒性概率折衷或参与重新评估方法标准。 In some trials with adaptive treatment assignment, the "exploration versus exploitation" problem may occur, wherein a \greedy" algorithm that always chooses the empirically optimal regime may get stuck at a regime that actually is inferior. To deal with this, we will explore hybrid algorithms that include adaptive randomization to reduce the risk of choosing inferior regimes. Computer simulation will be used as a design tool to calibrate design参数和先前参数以良好的频繁操作特征获得设计。在多个周期中，（3）通过未来数据加权随机分组来改善结果适应性临床试验的物流的算法，以及（4）基于三个结果的I-II期设计，以优化对早产儿的镇静剂量，用于治疗呼吸遇险综合征。将开发高效，用户友好，免费的计算机程序，以促进广泛的应用程序。