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Discriminant Function Analysis for Longitudinal Data: Applications in Medical Research (DiALog)

纵向数据的判别函数分析：在医学研究中的应用 (DiALog)

基本信息

批准号：
MR/L010909/1
负责人：
Marta Inmaculada Garcia-Finana
金额：
$ 42.58万
依托单位：
University of Liverpool
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FL010909%2F1
关键词：
Discriminant Function Analysis Longitudinal Data

项目摘要

Identifying the correct diagnosis as early as possible, especially before the patient presents clear structural and/or functional changes, is key for a successful treatment. For example, delays in the treatment of patients with suspected encephalitis can have a devastating impact, including severe cognitive disability. Hence, it is not surprising that in most medical disciplines research focuses on identifying biomarkers and risk factors, some of them measured over time, to correctly predict patient outcomes. We propose to develop a novel statistical methodology that allows us to identify patients at higher risk of developing a particular disease or condition sooner than is currently achieved, with subsequent benefits for patients, clinicians and for health care system costs.Delay in the detection of a disease is directly linked to economic burdens of the National Health Service and communities. When mechanisms are put in place to achieve early detection, they however may be economically demanding. Patients with diabetes, for instance, are screened annually for diabetic retinopathy at a considerable cost to the NHS. Bearing in mind that less than 4% of patients with diabetes will develop diabetic retinopathy within a year, there is a great interest in being able to identify patients with a higher risk in order to tailor the screening intervals. In other words, while patients with higher risk should be screened more often than once per year, the low risk group (involving 96% of the study population) could be screened less often than annually, reducing significantly both NHS costs and burden on patients and clinicians. Considering that in the UK alone there will be about 4 million people with diabetes by 2025, individualised screening is vital and it is expected to reduce annual costs to the NHS by more than £100 million without reducing medical screening efficacy. In addition to the importance of early diagnosis, being able to identify early that someone is likely to show a poor prognosis is also essential to improve clinical management and optimise resources. For example, approximately one third of patients with epilepsy do not achieve remission from seizures following drug therapy. Early identification of this patient group would allow clinicians to focus on alternative treatments (e.g., surgery) as early as possible. We aim to develop a novel time-dependent approach for discriminant analyss that (a) depends directly on both the individual baseline covariate information and the longitudinal data to achieve a more precise classification, (b) allows us to detect the earliest time point at which successful classification can be achieved (with a predefined error), and (c) can be applied to classify individuals into two or more groups, with unequal variance-covariance matrices among groups and incorporation of costs. Our objectives are divided into 3 main parts: development, implementation and clinical applications.

尽早确定正确的诊断，特别是在患者出现明确的结构和/或功能变化之前，是成功治疗的关键。例如，对疑似脑炎患者的治疗延误可能造成毁灭性影响，包括严重的认知障碍。因此，毫不奇怪，在大多数医学学科中，研究的重点是识别生物标志物和风险因素，其中一些是随着时间的推移而测量的，以正确预测患者的结果。我们建议开发一种新的统计方法，使我们能够比目前更快地识别出患有特定疾病或病症的高风险患者，从而为患者，临床医生和医疗保健系统成本带来后续效益。疾病发现的延迟与国家卫生服务和社区的经济负担直接相关。然而，当建立机制以实现早期发现时，它们可能在经济上要求很高。例如，糖尿病患者每年都要接受糖尿病视网膜病变筛查，这对NHS来说是一笔不小的开支。考虑到不到4%的糖尿病患者会在一年内发展为糖尿病视网膜病变，因此能够识别高风险患者以调整筛查间隔是非常有意义的。换句话说，虽然高风险患者应该每年筛查一次以上，但低风险组（涉及96%的研究人群）的筛查次数可以少于每年，这大大降低了NHS的成本和患者和临床医生的负担。考虑到到2025年，仅在英国就将有大约400万糖尿病患者，个性化筛查至关重要，预计在不降低医疗筛查效果的情况下，每年将为NHS减少超过1亿英镑的成本。除了早期诊断的重要性之外，能够早期识别可能表现出不良预后的人对于改善临床管理和优化资源也至关重要。例如，大约三分之一的癫痫患者在药物治疗后癫痫发作没有缓解。早期识别这一患者群体将使临床医生尽可能早地关注替代治疗（例如手术）。我们的目标是为判别分析开发一种新的时间相关方法，(a)直接依赖于个体基线协变量信息和纵向数据来实现更精确的分类，(b)允许我们检测最早的时间点，在这个时间点上可以实现成功的分类（具有预定义的误差），(c)可以应用于将个体分为两个或更多组。组间方差-协方差矩阵不等，并纳入成本。我们的目标分为三个主要部分：开发、实施和临床应用。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A comparison of group prediction approaches in longitudinal discriminant analysis.

DOI：
10.1002/bimj.201700013
发表时间：
2018-03
期刊：
Biometrical journal. Biometrische Zeitschrift
影响因子：
0
作者：
Hughes DM;El Saeiti R;García-Fiñana M
通讯作者：
García-Fiñana M

Personalized risk-based screening for diabetic retinopathy: A multivariate approach versus the use of stratification rules.

基于个性化风险的糖尿病性视网膜筛查：一种多元方法与使用分层规则的使用。

DOI：
10.1111/dom.13552
发表时间：
2019-03
期刊：
Diabetes, obesity & metabolism
影响因子：
0
作者：
García-Fiñana M;Hughes DM;Cheyne CP;Broadbent DM;Wang A;Komárek A;Stratton IM;Mobayen-Rahni M;Alshukri A;Vora JP;Harding SP
通讯作者：
Harding SP

A novel multivariate discriminant approach to predict sight threatening diabetic retinopathy (STDR) cases - data from the Liverpool Diabetic Eye Study

一种新颖的多变量判别方法来预测威胁视力的糖尿病视网膜病变 (STDR) 病例 - 来自利物浦糖尿病眼研究的数据