An investigation into the use of shrinkage methods to alleviate over-fitting of prognostic models for independent and clustered data with few events

研究使用收缩方法来减轻事件较少的独立数据和聚类数据的预后模型的过度拟合

• 批准号: MR/J013692/1
• 负责人: Rumana Omar
• 金额: $ 38.28万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FJ013692%2F1
• 关键词: investigation; into; use; shrinkage; methods

Clinicians, health service researchers and epidemiologists often wish to predict a future health outcome for patients and the public. Examples of such outcomes include development of coronary heart disease, the occurrence of in-hospital mortality following surgery, and the onset of depression. These predictions are used by clinicians to determine the prognosis of patients to plan their treatment, to detect high risk patients, and to provide information to patients enabling them to make decisions about their treatment options. Policy makers often use these predictions to assess the performance of hospitals and general practices and identify under performing institutions.Statistical models using patients' clinical and demographic characteristics are typically used to make these predictions. These models are referred to as prognostic models. To develop such models, information is collected on patients or relevant subjects, regarding their risk factors and the health outcome they experienced. The relationship between the risk factors and the outcome is quantified using a statistical model, which can then be used to make predictions for new patients. Models are usually presented in the form of a risk algorithm. This algorithm is then tested on new patients to ensure that it makes reliable predictions. If its performance is found to be satisfactory, it is recommended for use by clinicians in practice. Examples of risk algorithms used in practice include the Framingham risk score to predict the 10 year risk of coronary heart disease, Euroscore to predict in-hospital mortality following cardiac surgery and the PREDICT score to predict the risk of developing depression. When the health outcome of interest is rare it is often problematic to develop a risk algorithm that will both predict risk accurately and be able to classify patients into high and low risk groups. This is a common problem in health research and is often not alleviated by collecting patient data from many centres, or over a long period of time. A further statistical problem occurs with data from many centres as there may be variability in the outcomes between the centres. Robust models exist for relatively common events such as coronary heart disease, in-hospital mortality following cardiac surgery, and depression. However reliable prognostic models are scarce, or not available, for rarer health outcomes, for example death or recurrence following diagnosis of rare types of cancer, and the onset of Parkinson's disease. There is a similar problem when trying to develop prognostic models for common events in relatively small subgroups of people, for example, a model to predict coronary heart disease in people who have severe mental health problems. Some methodological research has been done to handle the problem of fitting statistical models for rare outcomes in genetic studies. However, limited work has been done to develop methods to produce reliable prognostic models with rare outcomes in clinical settings such as public health and health services research. Moreover, the methods that have been developed to date are not used routinely because of lack of software and adequate evaluation. There are currently no guidelines regarding how statisticians and other researchers should be using these methods in practice. The proposed research will evaluate the existing statistical methodology that is available to handle risk predictions when the health outcome of interest is rare, and will develop new methods where necessary. The proposed research will make recommendations regarding the use of these methods in practice. Additionally, the methods developed in this research project will be implemented in widely available statistical software to enable their routine use. The prognostic models developed using these methods should enable clinicians and policy makers to make predictions for patients regarding health outcomes, in these settings even if the outcome is rare

临床医生、卫生服务研究人员和流行病学家通常希望预测患者和公众未来的健康结果。此类结果的例子包括冠心病的发生、手术后院内死亡的发生以及抑郁症的发生。临床医生利用这些预测来确定患者的预后，以制定治疗计划、检测高风险患者，并向患者提供信息，使他们能够做出治疗选择的决定。政策制定者经常使用这些预测来评估医院和一般医疗机构的绩效，并识别绩效不佳的机构。通常使用使用患者临床和人口特征的统计模型来做出这些预测。这些模型被称为预后模型。为了开发这样的模型，需要收集患者或相关受试者的信息，包括他们的风险因素和他们经历的健康结果。使用统计模型对风险因素和结果之间的关系进行量化，然后可用于对新患者进行预测。模型通常以风险算法的形式呈现。然后在新患者身上测试该算法，以确保其做出可靠的预测。如果其性能令人满意，建议临床医生在实践中使用。实践中使用的风险算法的例子包括预测 10 年冠心病风险的 Framingham 风险评分、预测心脏手术后院内死亡率的 Euroscore 以及预测患抑郁症风险的 PREDICT 评分。当感兴趣的健康结果很少时，开发一种既能准确预测风险又能将患者分为高风险组和低风险组的风险算法通常会出现问题。这是健康研究中的一个常见问题，并且通常无法通过从许多中心或在很长一段时间内收集患者数据来缓解。来自许多中心的数据还存在进一步的统计问题，因为中心之间的结果可能存在差异。对于冠心病、心脏手术后的院内死亡率和抑郁症等相对常见的事件，存在稳健的模型。然而，对于罕见的健康结果，例如罕见类型癌症诊断后的死亡或复发以及帕金森病的发作，可靠的预后模型很少或不可用。当试图为相对较小的人群中的常见事件开发预后模型时，也存在类似的问题，例如，预测患有严重心理健康问题的人的冠心病的模型。已经进行了一些方法学研究来处理遗传研究中罕见结果的拟合统计模型的问题。然而，在公共卫生和卫生服务研究等临床环境中，在开发产生可靠预后模型的方法方面所做的工作有限，但结果很少。此外，由于缺乏软件和充分的评估，迄今为止开发的方法并未得到常规使用。目前还没有关于统计学家和其他研究人员如何在实践中使用这些方法的指南。拟议的研究将评估现有的统计方法，当感兴趣的健康结果很少见时，可用于处理风险预测，并将在必要时开发新方法。拟议的研究将对这些方法在实践中的使用提出建议。此外，该研究项目中开发的方法将在广泛使用的统计软件中实施，以实现日常使用。使用这些方法开发的预后模型应该使临床医生和政策制定者能够对患者的健康结果进行预测，即使结果很罕见

项目成果

How to develop a more accurate risk prediction model when there are few events.

• DOI: 10.1136/bmj.h3868
• 发表时间: 2015-08-11
• 期刊: BMJ (Clinical research ed.)
• 作者: Pavlou M;Ambler G;Seaman SR;Guttmann O;Elliott P;King M;Omar RZ
• 通讯作者: Omar RZ

Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events.

回顾和评估用于事件少的低维数据风险预测的惩罚回归方法。

• DOI: 10.1002/sim.6782
• 发表时间: 2016-03-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Pavlou M;Ambler G;Seaman S;De Iorio M;Omar RZ
• 通讯作者: Omar RZ

Use of Bayesian shrinkage for risk prediction in clustered data with few events

使用贝叶斯收缩对事件较少的聚类数据进行风险预测

• 发表时间: 2015
• 作者: Pavlou M

A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes.

• DOI: 10.1186/s12874-015-0046-6
• 发表时间: 2015-08-05
• 期刊: BMC medical research methodology
• 影响因子: 4
• 作者: Pavlou M;Ambler G;Seaman S;Omar RZ
• 通讯作者: Omar RZ