Data and Information Integration for Risk Prediction in the Era of Big Data
大数据时代的数据与信息融合风险预测
基本信息
- 批准号:10249251
- 负责人:
- 金额:$ 9.62万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-20 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAdoptedBig DataBiological MarkersBreast Cancer Risk FactorCalibrationClinical ResearchCommunitiesDataData SetEnsureEstrogensEvaluationFeasibility StudiesFruitGestational DiabetesGoalsHormonalIndividualLogistic RegressionsLogisticsMalignant NeoplasmsMeasuresMethodsModelingOutcomePatientsPlaguePopulationPublicationsResearch DesignRiskSamplingSampling StudiesSourceStatistical MethodsTarget PopulationsTimeValidationbasebiomarker evaluationbreast densitycase controlcost effectivedesigndisorder preventionepidemiology studyflexibilityimprovedinnovationmetabolomicsmodel buildingnoveloutcome predictionprecision medicinepredictive modelingpredictive testrisk predictionrisk prediction modeluser friendly software
项目摘要
Abstract
Toward precision medicine and precision disease prevention, the overarching goal of this proposal is to
develop innovative statistical methods for accurate risk prediction. We address three challenges that
plague studies on the value of candidate risk predictors that adds to established predictors for improved
predictive accuracy: there is often a lack of independent validation data, the source population for the
study sample and the target population of prediction are often different, no statistical methods are
currently available for developing risk prediction models using individually-matched case-control data,
and there is a lack of statistical methods for helping assess study feasibility beyond standard power
calculation for testing predictor-outcome association. On the other hand, data and information that are
external to the study may well exist and can be exploited to alleviate these challenges. For example, a
model with only standard predictors often exists and has been validated, and the distribution of standard
risk predictors in the target population of prediction is often available. We propose that external data and
information can be exploited to address the above-mentioned challenges for candidate predictor
evaluation, and develop innovative statistical methods to bring this idea to fruition. Considering
prediction of a binary outcome, we propose a novel method to building logistic prediction models that are
guaranteed to calibrate well in the target population, an innovative method for risk prediction with
individually matched case-control data, and a method to project the added value of candidate predictors to
help assess study feasibility. Our methods, accompanied by user-friendly software, will facilitate cost
effective and timely predictor evaluation for predicting binary outcomes. Our methods were motivated by
and will be applied to several PI Chen's collaborative studies.
摘要
在精准医疗和精准疾病预防方面,该提案的总体目标是
开发创新的统计方法,以准确预测风险。我们应对三大挑战,
关于候选风险预测因素的价值的瘟疫研究,这些预测因素增加了已建立的预测因素,
预测准确性:通常缺乏独立的验证数据,
研究样本和预测的目标人群往往不同,没有统计方法
目前可用于使用个体匹配的病例对照数据开发风险预测模型,
缺乏统计方法来帮助评估研究的可行性,
用于检验预测因子-结果关联的计算。另一方面,数据和信息,
研究的外部因素很可能存在,可以用来缓解这些挑战。例如是
只有标准预测因子的模型经常存在并得到验证,标准预测因子的分布
在预测的目标人群的风险预测往往是可用的。我们建议外部数据和
可以利用这些信息来解决候选预测器的上述挑战
评估,并制定创新的统计方法,使这一想法的成果。考虑
预测的二元结果,我们提出了一种新的方法来建立逻辑预测模型,
保证在目标人群中进行良好的校准,这是一种创新的风险预测方法,
个体匹配的病例对照数据,以及一种将候选预测因子的附加值投影到
帮助评估研究的可行性。我们的方法,加上用户友好的软件,将有助于降低成本,
有效和及时的预测因子评估,用于预测二元结局。我们的方法的动机是
并将应用于陈丕的几项合作研究。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Jinbo Chen其他文献
Jinbo Chen的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Jinbo Chen', 18)}}的其他基金
Data and Information Integration for Risk Prediction in the Era of Big Data
大数据时代的数据与信息融合风险预测
- 批准号:
10021609 - 财政年份:2019
- 资助金额:
$ 9.62万 - 项目类别:
Data and Information Integration for Risk Prediction in the Era of Big Data
大数据时代的数据与信息融合风险预测
- 批准号:
10480872 - 财政年份:2019
- 资助金额:
$ 9.62万 - 项目类别:
Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation Cancers
BRCA 1/2 突变癌症的癌症风险的精确评估和传递
- 批准号:
10228006 - 财政年份:2017
- 资助金额:
$ 9.62万 - 项目类别:
Enhancing Global Diversity in Cancer Clinical Genetics
增强癌症临床遗传学的全球多样性
- 批准号:
10164921 - 财政年份:2017
- 资助金额:
$ 9.62万 - 项目类别:
Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation Cancers
BRCA 1/2 突变癌症的癌症风险的精确评估和传递
- 批准号:
9762870 - 财政年份:2017
- 资助金额:
$ 9.62万 - 项目类别:
Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation Cancers
BRCA 1/2 突变癌症的癌症风险的精确评估和传递
- 批准号:
9381396 - 财政年份:2017
- 资助金额:
$ 9.62万 - 项目类别:
Precision Assessment and Delivery of Cancer Risks in BRCA 1/2 Mutation Cancers
BRCA 1/2 突变癌症的癌症风险的精确评估和传递
- 批准号:
9567099 - 财政年份:2017
- 资助金额:
$ 9.62万 - 项目类别:
Statistical Methods for Cancer Absolute Risk Prediction
癌症绝对风险预测的统计方法
- 批准号:
8503712 - 财政年份:2013
- 资助金额:
$ 9.62万 - 项目类别:
Statistical Methods for Cancer Absolute Risk Prediction
癌症绝对风险预测的统计方法
- 批准号:
8619604 - 财政年份:2013
- 资助金额:
$ 9.62万 - 项目类别:
Statistical Methods for Cancer Absolute Risk Prediction
癌症绝对风险预测的统计方法
- 批准号:
9052041 - 财政年份:2013
- 资助金额:
$ 9.62万 - 项目类别:
相似海外基金
How novices write code: discovering best practices and how they can be adopted
新手如何编写代码:发现最佳实践以及如何采用它们
- 批准号:
2315783 - 财政年份:2023
- 资助金额:
$ 9.62万 - 项目类别:
Standard Grant
One or Several Mothers: The Adopted Child as Critical and Clinical Subject
一位或多位母亲:收养的孩子作为关键和临床对象
- 批准号:
2719534 - 财政年份:2022
- 资助金额:
$ 9.62万 - 项目类别:
Studentship
A material investigation of the ceramic shards excavated from the Omuro Ninsei kiln site: Production techniques adopted by Nonomura Ninsei.
对大室仁清窑遗址出土的陶瓷碎片进行材质调查:野野村仁清采用的生产技术。
- 批准号:
20K01113 - 财政年份:2020
- 资助金额:
$ 9.62万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2633211 - 财政年份:2020
- 资助金额:
$ 9.62万 - 项目类别:
Studentship
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2436895 - 财政年份:2020
- 资助金额:
$ 9.62万 - 项目类别:
Studentship
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2633207 - 财政年份:2020
- 资助金额:
$ 9.62万 - 项目类别:
Studentship
A Study on Mutual Funds Adopted for Individual Defined Contribution Pension Plans
个人设定缴存养老金计划采用共同基金的研究
- 批准号:
19K01745 - 财政年份:2019
- 资助金额:
$ 9.62万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
The limits of development: State structural policy, comparing systems adopted in two European mountain regions (1945-1989)
发展的限制:国家结构政策,比较欧洲两个山区采用的制度(1945-1989)
- 批准号:
426559561 - 财政年份:2019
- 资助金额:
$ 9.62万 - 项目类别:
Research Grants
Securing a Sense of Safety for Adopted Children in Middle Childhood
确保被收养儿童的中期安全感
- 批准号:
2236701 - 财政年份:2019
- 资助金额:
$ 9.62万 - 项目类别:
Studentship
Structural and functional analyses of a bacterial protein translocation domain that has adopted diverse pathogenic effector functions within host cells
对宿主细胞内采用多种致病效应功能的细菌蛋白易位结构域进行结构和功能分析
- 批准号:
415543446 - 财政年份:2019
- 资助金额:
$ 9.62万 - 项目类别:
Research Fellowships