Statistical Methods for Complex Cancer Trials

复杂癌症试验的统计方法

基本信息

批准号：
7725789
负责人：
PETER F THALL
金额：
$ 15.4万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2000
资助国家：
美国
起止时间：
2000-04-03 至 2013-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7725789
关键词：
Accounting Address Area Bayesian Analysis Bayesian Method Budgets Characteristics Clinical Clinical Trials Clinical Trials Design Combined Modality Therapy Complex Computational algorithm Computer Simulation Computer software Data Data Set Development Disease Documentation Dose Drops Ensure Evaluation Funding Goals Heterogeneity Joints Malignant Neoplasms Mean Survival Times Methodology Methods Modeling Outcome Patients Phase Phase II/III Trial Phase III Clinical Trials Principal Investigator Probability Process Progression-Free Survivals Property Quality of life Research Sample Size Schedule Selection for Treatments Specific qualifier value Staging Statistical Methods Structure Subgroup Testing Time Toxic effect Treatment Efficacy Update Upper arm Writing base chemotherapeutic agent cohort design graphical user interface improved prognostic public health relevance research clinical testing simulation software development standard care treatment effect two-dimensional user friendly software vector

项目摘要

DESCRIPTION (provided by applicant): The proposed research is motivated by problems arising in the design and analysis of complex cancer clinical trials. A general goal is to accommodate clinical settings, data structures and scientific aims that are more complex than those addressed by designs in the conventional phase I phase II phase III paradigm. The proposed research deals with trials having sequential, outcome-adaptive decisions, including selecting what treatment, dose or schedule to give the next patient, deciding whether the trial should be stopped early, deciding whether accrual should be stopped for particular patient subgroups, and what final conclusions may be drawn about treatment effects. At each interim analysis during trial conduct, a Bayesian model is fit to the current data and posterior-based decision criteria are computed and applied. Each design's operating characteristics are evaluated by computer simulation under a set of scenarios, where a scenario is characterized by a fixed parameter vector of the assumed probability model, or under alternative probability distributions to study robustness. The scenarios are chosen to represent a suitably wide range of clinically meaningful cases. Operating characteristics include trial duration, number of patients assigned to each treatment or dose, and probabilities of possible decisions and conclusions. These evaluations are used to calibrate design parameters to ensure that the design has scientifically and ethically desirable properties. The proposed projects encompass a variety of clinical settings, including dose-finding trials, phase II-III trials, two- arm phase III trials with multiple outcomes, and any Bayesian setting where an informative prior is elicited. Models, methods, and computational algorithms will be developed for each of the following: (1) Choosing the optimal dose pair of a chemotherapeutic agent and a biologic agent used in combination. Patient outcome is a trinary vector of ordinal variables accounting for two types of toxicity, one associated with each agent, and treatment efficacy. Based on elicited numerical utilities of the possible patient outcomes, a dose pair is chosen for each successive patient cohort to maximize the current posterior mean utility. (2) Comparing multiple experimental treatments to standard therapy based on toxicity and progression-free-survival (PFS) time. The probability of a two-dimensional region based on elicited joint toxicity and PFS target probabilities will be used as the basis for treatment selection and confirmatory comparison, allowing the probability of toxicity and the PFS time distribution each to vary with patient covariates, while controlling overall generalized power and type I error. (3) Comparing treatments in a two-arm trial based on the trade-off between two outcomes such as, for example, quality of life and survival time. A general geometric method is proposed in which decisions are based on posterior probabilities of four sets that parition a two-dimensional parameter space. (4) Using nonlinear regression to estimate prior hyperparameters from elicited information. The goal is to provide a general, practical method for establishing priors in a Bayesian analysis based on information elicited from area experts in settings where the number of pieces of elicited information is much larger than the number of hyperparameters characterizing the prior. PUBLIC HEALTH RELEVANCE: The proposed research will provide more efficient and more ethically desirable designs for complex cancer clinical trials by making formal use of historical data, using individualized, patient-specific rules for treatment assignment and early stopping, and combining successive phases of treatment evaluation. The improved efficiencies will accelerate clinical evaluation and increase the likelihood that new treatments providing a clinical improvement over standard therapies will be detected while unpromising or unsafe treatments will be dropped.

描述（由申请人提供）：拟议的研究是由复杂癌症临床试验的设计和分析中产生的问题引起的。一个总体目标是适应比在常规I期II期II阶段III期范式中设计的临床环境，数据结构和科学目标更为复杂。拟议的研究涉及具有顺序，自适应决定的试验，包括选择给下一个患者的治疗，剂量或时间表，确定是否应尽早停止试验，确定是否应为特定患者子组停止应计，以及在治疗效果方面可能得出哪些最终结论。在试验过程中的每个临时分析中，贝叶斯模型符合当前数据，并计算和应用后验决策标准。每个设计的操作特性都通过计算机模拟在一组方案下进行评估，其中场景以假定概率模型的固定参数向量为特征，或者在替代概率分布中以研究鲁棒性。选择这些方案以代表适当的临床有意义的病例。操作特征包括试验持续时间，分配给每种治疗或剂量的患者数量以及可能的决策和结论的概率。这些评估用于校准设计参数，以确保设计具有科学和道德上理想的特性。拟议的项目涵盖了各种临床环境，包括剂量发现试验，II-III期试验，具有多个结果的两臂III期试验以及引起信息性之前的任何贝叶斯环境。将针对以下每一个开发模型，方法和计算算法：（1）选择最佳剂量对化学治疗剂和用于组合使用的生物学剂。患者的结果是序数变量的三位载体，该量值考虑了两种类型的毒性，一种与每种药物有关，以及治疗功效。基于可能患者结果的引起的数值实用程序，为每个连续的患者队列选择一个剂量对，以最大化当前的后平均效用。（2）根据毒性和无进展生存（PFS）时间比较多种实验疗法与标准治疗时间。基于引起的关节毒性和PFS目标概率的二维区域的概率将被用作治疗选择和确认性比较的基础，从而使毒性的概率和PFS时间分布都随患者协变量而变化，同时控制了总体通用I误差。（3）基于两个结果之间的权衡，例如生活质量和生存时间，比较两臂试验的治疗方法。提出了一种一般的几何方法，其中决策是基于四组二维参数空间的四组的后验概率。（4）使用非线性回归从引起的信息中估算先前的超参数。目的是提供一种一般，实用的方法，以基于从区域专家在设置中引起的信息的信息来建立先验，其中引起的信息的数量比表征先前表征的超参数数量要大得多。公共卫生相关性：拟议的研究将通过正式使用历史数据，使用个性化的，特定于患者的治疗分配和早期停止，并结合治疗评估的连续阶段，为复杂的癌症临床试验提供更有效和更具道德要求的设计。提高的效率将加速临床评估，并增加提供对标准疗法进行临床改善的新疗法的可能性，而在不促进或不安全的治疗方法将被放弃。