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.

项目概要 准确描述哪些患者从阿尔茨海默病或艾滋病毒治疗或项目中受益匪浅 缓解对于了解哪些治疗方法对哪些患者有效以及为其他患者制定有效的计划至关重要。一个 对此的主要挑战是这些疾病的异质性。迄今为止，阿尔茨海默病的治疗研究 患有合并症的药物几乎没有显示任何功效。此外，对于资源有限环境中的艾滋病毒/艾滋病患者来说， 只有一小部分人使用抗逆转录病毒治疗 (ART) 或从特定计划中受益以提高 ART 的接受率。 描述哪些患者从此类治疗/计划中受益的标准方法，首先构建一个预测 使用标准统计标准进行预测，然后使用该预测因子来描述高效益患者的特征。对于这样的 因此，临床目标——描述高效益患者的特征——仅在实施时被考虑。 化阶段，并且不用于该方法的构造。在早期的工作中，我们已经证明这种方法 可能会严重歪曲高收益患者；我们开发了一种直接链接的方法 将临床目标（高效益）纳入表征机制的构建中。我们的动机是： 减少阿尔茨海默病患者躁动的研究；以及一项提高艾滋病毒患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 与这种新型的简单方法相比，患者的获益提高了两倍或更多。 在本提案中，我们将开发并应用此类新的临床针对性统计方法来表征 高效益患者。这些方法将使医生和患者能够更好地选择最佳治疗方法 和计划，有可能使数百万患者受益。所提出的方法是为了三个目标而开发的， 并在初步工作之后，受到阿尔茨海默氏症和艾滋病毒研究的推动并将应用于这些研究。 目标 1. 开发方法来表征从随机对照治疗中获益匪浅的患者 被控制的审判。这些方法很重要，因为它们可以识别出可能被遗漏的高效益患者 当使用标准方法时。 目标 2. 开发方法来描述随机试验中高获益和高风险患者的特征。 在这里，我们将开发方法来描述高获益患者和高风险患者的特征 不良事件。这些方法可以让患者更好地平衡治疗的风险和收益。 目标 3. 开发方法来描述从观察治疗中获益匪浅的患者 研究。我们将使用方法将观察性研究转变为尽可能接近随机研究的研究 一，然后我们可以扩展目标 1 和 2 的方法。这些方法在随机化的情况下很重要 试验并不容易进行。他们将使用上述两项研究以及总统紧急救援计划 (PEPFAR) 进行测试 艾滋病紧急救援计划）网站，以描述从该计划中获得低收益的患者， 以便为他们提供额外的支持。