Statistical methods for characterizing patients who highly-benefit from treatments and programs in Alzheimers, HIV, and other heterogeneous diseases

用于描述从阿尔茨海默病、艾滋病毒和其他异质性疾病的治疗和项目中受益匪浅的患者的统计方法

• 批准号: 9919323
• 负责人: CONSTANTINE E FRANGAKIS
• 金额: $ 44.45万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-04 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9919323
• 关键词: AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Agitation; Alzheimer' s Disease; American; Area; Benefits and Risks; Citalopram; Clinical; Data; Dementia; Disease; Emergency Situation; Equilibrium; Feedback; Goals; HIV; Heterogeneity; Human; Intervention; Link; Methods; Modeling; Observational Study; Patients; Physicians; Placebos; Randomized; Randomized Controlled Trials; Resources; Serious Adverse Event; Site; Statistical Methods; Symptoms; Testing; Uncertainty; Vietnam; Work; adverse event risk; antiretroviral therapy; base; comorbidity; cost; disease heterogeneity; effective intervention; high risk; intervention effect; patient subsets; profiles in patients; programs; randomized trial; standard care; treatment program; uptake

Project Summary Accurate characterization of which patients benefit highly from a treatment or program in Alzheimer's or HIV dis- eases are central for knowing which treatments work for which patients, and to plan effectively for the others. A major challenge for this is heterogeneity of these diseases. Until now, treatment studies for Alzheimer's disease with comorbidities have shown little if any efficacy. Also, for HIV/AIDS patients in resource -constrained settings, only a small fraction use antiretroviral treatment (ART) or benefit from a given program to increase ART uptake. Standard methods to characterize which patients benefit from such treatments/programs, first construct a predic- tor using standard statistical criteria, and then use that predictor to characterize high-benefit patients. For such methods, therefore, the clinical goal – to characterize high-benefit patients – is considered only at the implemen- tation stage, and is not used for the construction of the method. In earlier work, we have shown that such methods can dramatically misrepresent high-benefit patients; and we have developed a type of method that directly links the clinical goal (high benefit) into the construction of the characterization mechanism. We were motivated by: a study to reduce agitation in patients with Alzheimer's disease; and a study to increase ART uptake among HIV patients in Vietnam. We have shown that methods that lack this clinical link can miss and underestimate high benefit patients by a factor of 2 or more, compared to even simple methods of this new type. In this proposal, we will develop and apply such new clinically-targeted statistical methods for characterizing high-benefit patients. Such methods will allow physicians and patients to make better choices of best treatments and programs, with potential to benefit millions of patients. The proposed methods are developed for three aims, and, following the preliminary work, are motivated by and will be applied to Alzheimer's and HIV studies. Aim 1. Develop methods to characterize patients who highly benefit from treatment in randomized con- trolled trials. These methods are significant because they can identify high benefit patients who would be missed when using standard methods. Aim 2. Develop methods to characterize patients with high benefit and high risk in randomized trials. Here, we will develop methods to characterize, patients with high benefit, among those with high risk of an adverse event. These methods can allow patients to better balance risk and benefit of treatments. Aim 3. Develop methods to characterize patients who highly benefit from treatment in observational studies. We will use methods to transform observational studies to a study as close as possible to a randomized one, where we can then extend the methods of aims 1 and 2. These methods are significant where randomized trials are not easy to conduct. They will be tested using the above two studies, and also at a PEPFAR (President's Emergency Program for AIDS Relief) site, to characterize patients who receive low benefit from the program, in order to provide to them extra support.

项目摘要 准确描述哪些患者从阿尔茨海默病或艾滋病的治疗或计划中贝内匪浅， 病例是了解哪些治疗对哪些患者有效以及为其他患者有效计划的核心。一 这方面的主要挑战是这些疾病的异质性。到目前为止，阿尔茨海默病的治疗研究 有合并症的患者几乎没有显示出任何疗效。此外，对于资源有限环境中的艾滋病毒/艾滋病患者， 只有一小部分人使用抗逆转录病毒治疗（ART）或贝内于增加ART吸收的特定计划。 标准的方法来表征哪些患者贝内于这种治疗/计划，首先构建一个预测， 使用标准的统计标准，然后使用该预测因子来表征高效益患者。对于这样 方法，因此，临床目标-表征高效益患者-仅在实施时考虑， 站阶段，并且不用于该方法的构造。在早期的工作中，我们已经表明，这种方法， 可以大大歪曲高效益的病人;我们已经开发出一种方法，直接联系 将临床目标（高贝内）纳入表征机制的构建中。我们的动机是：a 一项旨在减少阿尔茨海默病患者躁动的研究;以及一项旨在增加艾滋病毒感染者接受抗逆转录病毒治疗的研究 越南的病人我们已经证明，缺乏这种临床联系的方法可能会错过和低估高风险。 与这种新类型的简单方法相比，它使患者贝内2倍或更多。 在这项提案中，我们将开发和应用这种新的临床靶向统计方法， 高效益患者。这样的方法将使医生和患者能够更好地选择最佳治疗方法 和项目，有可能使数百万患者贝内。所提出的方法是为了三个目的而开发的， 并且，在初步工作之后，被激发并将被应用于阿尔茨海默氏症和艾滋病研究。 目标1.制定方法来描述在随机对照中从治疗中高度贝内的患者， trolled审判这些方法很重要，因为它们可以识别出可能被遗漏的高贝内患者。 当使用标准方法时。 目标2.制定方法来描述随机试验中高贝内和高风险患者的特征。 在这里，我们将开发方法来描述高贝内患者，以及高风险患者。 不良事件。这些方法可以让患者更好地平衡治疗的风险和贝内。 目标3.制定方法，以描述从观察性治疗中高度贝内的患者 问题研究我们将使用方法将观察性研究转化为尽可能接近随机化的研究 第一，我们可以扩展目标1和2的方法。这些方法在随机化的情况下很重要 审判并不容易进行。他们将使用上述两项研究进行测试，并在PEPFAR（总统的 艾滋病紧急救援计划）网站，以描述从该计划中获得低贝内的患者， 为他们提供额外的支持。