CAREER: Online Multiple Hypothesis Testing: A Comprehensive Treatment

职业：在线多重假设检验：综合治疗

基本信息

批准号：
1945266
负责人：
Aaditya Ramdas
金额：
$ 40万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945266&HistoricalAwards=false
关键词：
CAREER Online Multiple Hypothesis Testing

项目摘要

It is common in the technological and pharmaceutical industries to test a large sequences of hypotheses over time. As an example in the latter case, suppose a lab is trying to develop a cure for a disease like Alzheimer's. This is a complex disease for which it is unlikely to find a single cure that works for everyone. It is much more likely that research on the drug will continue for years, if not decades, and every few months a new drug may be tested for its efficacy using a clinical trial. When we are testing whether a particular drug is any better than a placebo, we have no idea how many more drugs (hypotheses) we will test in the future, but we do know the results of the earlier tests. This is the setup considered by online multiple hypothesis testing, the topic of this project --- a large sequence of hypotheses are tested over time in an online fashion, and we would like to ensure that there are not too many false discoveries in this process just due to chance. A false discovery results not just in false hopes, but in millions of wasted dollars in follow up clinical trials, and possibly worse outcomes for patients. This project aims to develop novel methodology to test such a sequence of hypotheses so that certain common error metrics are controlled at any time. The training component for undergraduate and graduate students will prepare new researchers with inter-disciplinary education via the planned cross-disciplinary tutorials/workshops, and outreach to K-12 students.The methodology in offline multiple testing is rich, with a plethora of methods that control a wide variety of error metrics, and in fact the PI has contributed significantly to the literature recently. In contrast, the online multiple testing literature is less developed. This grant takes a holistic and comprehensive approach, that will result in new methods for a whole spectrum of error metrics: global null testing, family wise error rate, false discovery rate, false coverage rate, and simultaneous control of the false discovery proportion. The PI already has preliminary work on some of these fronts. We will also develop a public software package in R along with associated documentation to enable the easier assimilation and application of these methods. All methods will be accompanied by rigorous theoretical guarantees, is would be desirable in the aforementioned pharmaceutical application.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着时间的推移，测试一系列假设的技术和制药行业通常很常见。在后一种情况下，假设一个实验室试图为像阿尔茨海默氏病这样的疾病开发一种治疗方法。这是一种复杂的疾病，不太可能找到适合所有人的单一治疗方法。关于该药物的研究将持续数年，即使不是几十年，每隔几个月就可以使用临床试验对其有效性进行测试。当我们测试特定药物是否比安慰剂更好时，我们不知道将来会测试多少种药物（假设），但是我们确实知道早期测试的结果。这是通过在线多个假设测试（该项目的主题）考虑的设置 - 随着时间的流逝，一系列假设经过一系列假设的测试，我们希望确保在此过程中没有太多的错误发现仅是由于机会。错误的发现不仅是错误的希望，而且在随访的临床试验中浪费了数百万美元，患者的预后可能更糟。该项目旨在开发新的方法来检验这样一系列假设，以便随时控制某些常见的误差指标。本科和研究生的培训成分将通过计划的跨学科教程/研讨会和向K-12学生推广的新研究人员接受跨学科教育的新研究人员。离线多重测试中的方法很丰富，多种方法具有多种方法，这些方法可以控制多种错误的指标，以及PI在最近的贡献中贡献了很大的贡献。相比之下，在线多个测试文献的发展较低。该赠款采用了整体和全面的方法，这将为整个错误指标提供新的方法：全球无效测试，家庭明智的错误率，错误的发现率，错误的覆盖率和同时控制错误发现比例。 PI已经在其中一些方面进行了初步工作。我们还将在R中开发一个公共软件包以及关联的文档，以使这些方法更容易同化和应用。所有方法都将伴随着严格的理论保证，在上述药品应用中是可取的。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估来评估值得支持的。