Collaborative Research: Subject-level Prediction and Application

合作研究：学科级预测与应用

• 批准号: 1914465
• 负责人: Jiming Jiang
• 金额: $ 10万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914465&HistoricalAwards=false
• 关键词: Collaborative; Research; Subject; level; Prediction

Many practical problems are related to prediction, where the main interest is at the subject (for example personalized or precision medicine) or (small) sub-population (for example small community) level. In recent years, new and challenging problems have emerged from diverse fields such as business, social sciences, and health sciences. Examples may involve prediction of a health outcome for a new patient or perhaps prediction of a new school's response to efforts to educate children about smoking prevention. The investigators have shown in previous work called classified mixed model prediction (CMMP) that in such cases, it is possible to make substantial gains in prediction accuracy by identifying a class that a new subject belongs to. However, the scenarios under which CMMP currently operates are somewhat constrained and many real-life situations fall outside its scope. Given the tremendous gains in accuracy that are possible, it would be very valuable to develop further methodology and computational advances to deepen knowledge in this area. This project aims to make methodological advances of the classified mixed model prediction method into other types of subject-level prediction problems as well as to develop new inferential methods along the CMMP idea, by making the latter truly useful in practical situations. The basic idea of CMMP is to create a "match" between a group or cluster in the population for which one wishes to make prediction and a (massive) training dataset, with known groups or clusters. Once such a match is built, the traditional mixed model prediction method can be utilized to make accurate predictions. The practical challenges that will be solved in this project include i) how to deal with training data with unknown grouping; ii) how to deal with sparse, high dimensional covariates; iii) how to make better use of covariate information to improve accuracy of CMMP; and iv) how to provide accurate measures of uncertainty for CMMP-type predictions. Two important areas of application will be investigated. One is in precision medicine and health disparities focusing on the prediction of epigenetic markers using high dimensional genotype profiles. The other comes from the area of family economics using a large survey of data from China where predictions at finer levels of resolution (e.g., households) are of primary interest. Both applications will leverage important collaborations with practitioners and thus increase the impact of the work.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.

许多实际问题与预测有关，其中主要关注的是主题（例如个性化或精确医学）或（小）子群体（例如小社区）水平。 近年来，商业、社会科学和健康科学等不同领域出现了新的和具有挑战性的问题。 例如，可能涉及对新患者的健康结果的预测，或者可能涉及对新学校对教育儿童预防吸烟的反应的预测。 研究人员在之前的分类混合模型预测（CMMP）中表明，在这种情况下，通过识别新受试者所属的类别，可以大幅提高预测准确性。 然而，CMMP目前运行的场景受到一定的限制，许多现实生活中的情况超出了其范围。 鉴于可能在准确性方面取得的巨大进展，开发进一步的方法和计算进步以深化这一领域的知识将是非常有价值的。 本项目的目的是使分类混合模型预测方法在其他类型的主题级预测问题中取得方法上的进步，并通过使CMMP在实际情况中真正有用，沿着CMMP思想开发新的推理方法。 CMMP的基本思想是在希望进行预测的群体中的组或聚类与具有已知组或聚类的（大规模）训练数据集之间创建“匹配”。一旦建立了这样的匹配，就可以利用传统的混合模型预测方法来进行准确的预测。该项目将解决的实际挑战包括：i）如何处理具有未知分组的训练数据; ii）如何处理稀疏的高维协变量; iii）如何更好地利用协变量信息来提高CMMP的准确性;以及iv）如何为CMMP类型的预测提供准确的不确定性度量。将研究两个重要的应用领域。 一个是在精准医学和健康差异方面，重点是使用高维基因型谱预测表观遗传标记。另一个来自家庭经济学领域，使用来自中国的大规模数据调查，在更精细的分辨率水平上进行预测（例如，家庭是主要的利益。这两个应用程序将利用与从业者的重要合作，从而增加工作的影响。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Assessing uncertainty for classified mixed model prediction

评估分类混合模型预测的不确定性

• DOI: 10.1080/00949655.2021.1955885
• 发表时间: 2022
• 期刊: Journal of Statistical Computation and Simulation
• 影响因子: 1.2
• 作者: Nguyen, Thuan;Jiang, Jiming;Sunil Rao, J.
• 通讯作者: Sunil Rao, J.

A new classified mixed model predictor

一种新的分类混合模型预测器

• DOI: 10.1016/j.jspi.2019.11.001
• 发表时间: 2020
• 期刊: Journal of Statistical Planning and Inference
• 影响因子: 0.9
• 作者: Sun, Hanmei;Luan, Yihui;Jiang, Jiming
• 通讯作者: Jiang, Jiming

Classified generalized linear mixed model prediction incorporating pseudo‐prior information

结合伪先验信息的分类广义线性混合模型预测

• DOI: 10.1002/cjs.11727
• 发表时间: 2023
• 期刊: Canadian Journal of Statistics
• 作者: Ma, Haiqiang;Jiang, Jiming
• 通讯作者: Jiang, Jiming

Pseudo-Bayesian Classified Mixed Model Prediction

伪贝叶斯分类混合模型预测

• DOI: 10.1080/01621459.2021.2008944
• 发表时间: 2023
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Ma, Haiqiang;Jiang, Jiming
• 通讯作者: Jiang, Jiming