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氏症或fi疾病的治疗或计划中受益匪浅 要知道哪些治疗对哪些患者有效，并有效地为其他患者制定计划，病例是核心。一个 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 与合并症几乎没有表现出EffiCacy。此外，对于资源有限的艾滋病毒/艾滋病患者， 只有一小部分人使用抗逆转录病毒治疗(ART)或从特定计划中获得BBenefit来增加ART的摄取率。 为了确定哪些患者从这些治疗/计划中受益，fifi首先构建一个预测-- TOR使用标准统计标准，然后使用预测因子来表征高危fi测试患者。对于这样的 方法因此，临床目标--确定高效益fi患者的特征--仅在实施时被考虑。 转换阶段，而不是用于构造该方法。在早期的工作中，我们已经证明了这些方法 可以极大地歪曲高效益fit患者；我们已经开发出一种方法，直接将 将临床目标(HIGH BENEfit)纳入表征机制的构建。我们的动机是：a 一项减少阿尔茨海默病患者焦虑的研究；以及一项增加艾滋病毒患者抗逆转录病毒药物摄入量的研究 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遗漏并低估高估。 与这种新类型的简单方法相比，Benefit患者减少了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目标统计方法来表征 高Be-fi患者。这种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，可能使数以百万计的患者受益于fi。提出的方法是为了三个目的而开发的， 并且，在前期工作之后，受到阿尔茨海默氏症和艾滋病毒研究的激励，并将应用于该研究。 目的1.发展在随机对照试验中描述高度受益于fi治疗的患者的方法。 巨魔般的审判。这些方法很有意义，因为它们可以识别出可能会被遗漏的高效益fifit患者。 当使用标准方法时。 目的2.建立在随机试验中确定高效益fit和高风险患者的方法。 在这里，我们将开发一种方法来鉴定高危人群中的高fit患者。 不良事件。这些方法可以让患者更好地平衡风险和治疗的好处fit。 目的3.发展在观察性治疗中对高度受益于fit的患者进行表征的方法 学习。我们将使用方法将观察性研究转变为尽可能接近随机的研究 一，然后我们可以扩展目标1和2的方法。这些方法是显著的fi不能随机化 审判并不容易进行。他们将使用上述两项研究进行测试，也将在PEPFAR(总统的 艾滋病紧急救援计划)网站，以表征从该计划中获得低福利fit的患者，在 以便为他们提供额外的支持。