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

尽早确定正确的诊断，特别是在患者出现明显的结构和/或功能变化之前，是成功治疗的关键。例如，延误疑似脑炎患者的治疗可能会产生毁灭性的影响，包括严重的认知障碍。因此，在大多数医学学科中，研究侧重于识别生物标记物和风险因素，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也就不足为奇了。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特定疾病或病情的更高风险的患者，从而为患者、临床医生和医疗保健系统成本带来好处。疾病检测的延迟与国家医疗服务和社区的经济负担直接相关。然而，当建立机制以实现及早发现时，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的筛查，这对NHS来说是一笔相当大的费用。考虑到不到4%的糖尿病患者将在一年内发展为糖尿病视网膜病变，人们对能够识别风险较高的患者以便定制筛查间隔非常感兴趣。换句话说，虽然风险较高的患者应该每年进行一次以上的筛查，但低风险组(涉及96%的研究人群)进行筛查的频率可以少于每年一次，从而显著降低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，NHS每年的成本将减少1亿多GB。除了早期诊断的重要性外，能够及早识别某人可能表现出不良预后也是改善临床管理和优化资源的关键。例如，大约三分之一的癫痫患者在药物治疗后无法从癫痫发作中得到缓解。及早识别这一患者群体将使临床医生能够尽早专注于替代治疗(例如手术)。我们的目标是开发一种新的时间依赖的判别分析方法，该方法(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)允许我们检测可以实现成功分类的最早时间点(具有预先定义的误差)，以及(C)可以用于将个体分类到两个或更多组，组间的方差-协方差矩阵不相等并结合成本。我们的目标分为3个主要部分：开发、实施和临床应用。

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.