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，延误对疑似脑炎患者的治疗可能会产生破坏性影响，包括严重的认知障碍。因此，在大多数医学学科中，研究重点是确定生物标志物和风险因素，其中一些随着时间的推移而测量，以正确预测患者的结果，这并不奇怪。我们建议开发一种新的统计方法，使我们能够识别出患者在更高的风险，发展一种特定的疾病或条件比目前实现的更快，随后的好处，为患者，临床医生和医疗保健系统costs. The延迟检测的疾病是直接联系在一起的国家卫生服务和社区的经济负担。然而，如果建立了实现早期检测的机制，则可能会在经济上有很高的要求。例如，每年对糖尿病患者进行糖尿病视网膜病变筛查，这对NHS来说是一笔可观的费用。考虑到不到4%的糖尿病患者会在一年内发生糖尿病视网膜病变，因此能够识别具有较高风险的患者以定制筛查间隔是非常重要的。换句话说，虽然风险较高的患者应每年筛查一次以上，但低风险组（涉及96%的研究人群）的筛查频率可以低于每年，从而显着降低NHS成本以及患者和临床医生的负担。考虑到仅在英国，到2025年将有约400万糖尿病患者，个性化筛查至关重要，预计将在不降低医疗筛查效果的情况下，将NHS的年度成本降低1亿多英镑。除了早期诊断的重要性之外，能够早期识别某人可能表现出不良预后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没有达到癫痫发作的缓解。对这一患者群体的早期识别将使临床医生能够专注于替代治疗（例如，手术尽早进行。我们的目标是开发一种新的时间依赖的判别分析方法，（a）直接依赖于个体基线协变量信息和纵向数据，以实现更精确的分类，（B）允许我们检测成功分类的最早时间点（具有预定义的误差），以及（c）可以应用于将个体分类为两个或更多个组，其中组之间的方差-协方差矩阵不相等，并且并入成本。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.