Statistical Learning and Adaptive Observation in Clinical Prediction: Methodology and Applications

项目描述：(最多4,000个字符)临床预测模型(CPM)可以根据已知的个人情况预测感兴趣事件的发生，可以实现个性化和预防性的医疗保健方法。然而，现有的CPM通常是基于横断面数据的，从而忽略了病历中丰富的纵向信息。因此，探索将纵向数据纳入CPM的方法是一个活跃的研究领域。一个问题是，记录纵向观察的位置/设置(例如，初级保健或二级保健)可能会影响记录的观察，如果没有适当地考虑，这可能会给CPM带来偏见。此外，有限的资源限制了医疗保健专业人员观察患者纵向信息的频率；原则上，CPM可以用于对患者的高频和低频观察的适应期进行分类，但在实践中可以这样做之前，需要进行方法学研究。因此，这个博士项目将开发统计方法，以允许从患者层面的行为和观察模式中“学习”出CPM。该项目最初有两个调查途径。首先，我们将探索知道风险因素是如何以及在哪里被测量的是否有助于改善临床结果的预测(测量误差视角)。其次，我们将寻求开发概率建模框架，以评估是否有不同类别的患者需要分层/个性化干预。其他调查领域将包括数据样本大小、预测悖论、知情存在。

Project Description: (maximum of 4,000 characters)Clinical prediction models (CPMs), which predict the occurrence of an event of interest given what is known about an individual, could enable a personalized and preventative approach to healthcare. However, existing CPMs are usually based on cross-sectional data, thereby ignoring the rich longitudinal information in medical records. As such, exploring ways of incorporating longitudinal data into CPMs is an active area of research. One problem is that the location/setting of where longitudinal observations are recorded (e.g. primary care or secondary care) can influence the recorded observations, which can introduce bias into the CPMs if not properly accounted for. Additionally, limited resources restrict how frequently healthcare professionals can observe longitudinal information about patients; in principle, CPMs could be used to triage adaptive periods of high- and low-frequency observation for patients, but methodological research is required before this can be done in practice. Therefore, this PhD project will develop statistical methods to allow the derivation of CPMs that "learn" from patient-level behaviours and observation patterns. The project has initially two avenues of investigation. Firstly, we will explore if knowing how and where a risk factor has been measured leads to improved prediction of clinical outcomes (measurement error perspective). Secondly, we will look to develop probabilistic modelling frameworks to evaluate whether there are heterogeneous groups of patients who need stratified/personalised interventions. Additional areas of investigation will be data sample size, prediction paradox, informed presence.