Optimal treatment policies and adaptive screening for functional predictors

基于生物医学图像、质谱分析或基因表达的高维患者特征有朝一日可能会用于指导治疗选择和改善结果。 该项目的第一部分致力于开发新的统计方法，以评估基于这种高维度特征的个性化治疗政策的有效性。 该方法涉及指定的治疗和患者的个人资料之间的相互作用的功能回归模型，因此，从随机临床试验的数据可以被用来同时评估治疗政策的有效性，测量的平均结果时，所有患者遵循的政策，并确定功能的患者配置文件，优化相互作用的影响，在竞争的治疗。 该项目的第二部分涉及一种新的方式校准筛选程序的基础上边缘回归检测存在的显着预测在高维配置文件。 已知标准推理方法在此设置中失败，这是由于所选预测因子的估计回归系数的非规则限制行为。 为了避免这种非规律性，一个新的自助校准程序的开发，以更好地反映小样本的行为。 尽管在过去的10-20年中，已经有许多用于分析高维患者特征数据的方法，但它们主要是为了找到患者结局的关键预测因素。相对较少的关注已经支付给评估的个性化治疗政策，以优化患者的结果的价值的问题。该项目的主要创新之处在于，开发了新的方法来估计这种最佳决策规则在预期患者结果方面的价值。 此外，一个新的自适应reserve测试程序的开发，以解决一个中心问题，在高维筛选计算p值的方式，适应后模型选择的参数估计的固有不稳定性。 该项目具有更广泛的影响，涉及生物医学成像，质谱和高通量基因表达技术的最新进展，所有这些都产生了大量的个体患者数据。 有效利用这些数据有可能为个体患者提供量身定制的治疗。所提出的方法可以应用于例如脑成像数据以设计抑郁症的治疗，PET研究比较用认知疗法治疗的患者和用抗抑郁药治疗的患者以确定哪种治疗更有可能使给定患者受益，质谱分析用于检测癌症病例和对照之间的差异，以可能有助于个性化癌症护理的方式，以及用于设计癌症或心血管疾病的个体化疗法的基因表达谱。 另一个更广泛的影响是博士的培养。学生由PI和Co-PI指导，并在新的研究生课程模块的开发，旨在引入博士学位。学生可以通过函数数据分析和推理来获得最佳治疗策略。

High-dimensional patient profiles based on biomedical images, mass spectrometry, or gene expression, might one day be used to guide treatment selection and improve outcomes. The first part of the project is devoted to the development of new statistical methodology for assessing the effectiveness of individualized treatment policies based on such high-dimensional profiles. The approach involves specifying the interaction between the treatment and patient profile in terms of a functional regression model, so data from randomized clinical trials can be utilized to simultaneously evaluate the effectiveness of the treatment policies, measured in terms of mean outcome when all patients follow the policy, and to identify features of patient profiles that optimize the interaction effect over competing treatments. The second part of the project concerns a new way of calibrating screening procedures based on marginal regression for detecting the presence of significant predictors in high-dimensional profiles. Standard inferential methods are known to fail in this setting due to the non-regular limiting behavior of the estimated regression coefficient of selected predictors. To circumvent this non-regularity, a new bootstrap calibration procedure is developed in order to better reflect small-sample behavior. Although many methods for analyzing high-dimensional patient profile data have become available over the last 10-20 years, they are primarily for the purpose of finding the key predictors of patient outcomes. Relatively little attention has been paid to the problem of assessing the value of individualized treatment policies to optimize patient outcomes. The major innovation of the project is that new ways of estimating the value of such optimal decision rules in terms of expected patient outcomes are developed. In addition, a new adaptive resampling test procedure is developed to address a central problem in high-dimensional screening by computing p-values in a way that adapts to the inherent instability of post-model-selected parameter estimates. The project has broader impacts related to recent advances in biomedical imaging, mass spectrometry, and high-throughput gene expression technology, all of which produce massive amounts of data on individual patients. The effective use of such data has the potential to open up the possibility of tailoring treatments to individual patients. The proposed methods could be applied, for example, to brain imaging data to design treatments for depression, to PET studies that compare patients treated with cognitive therapy and patients treated with anti-depressants in order to determine which treatment is more likely to benefit a given patient, to mass spectrometry profiling for detecting differences between cancer cases and controls in a way that may contribute to personalized cancer care, and to gene expression profiles for designing individualized therapies for cancer or cardiovascular disease. Another broader impact is in the training of Ph.D. students mentored by the PI and Co-PI, and in the development of modules for new graduate courses designed to introduce Ph.D. students to functional data analysis and inference for optimal treatment policies.