Methods for parameter-driven and wait time models

参数驱动和等待时间模型的方法

• 批准号: 293140-2011
• 负责人: Altman, Rachel
• 金额: $ 0.95万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635927
• 关键词: Methods; parameter; driven; wait; time

My research is largely motivated by medical applications, and involves three broad areas of statistics: designs for multiple sclerosis (MS) clinical trials, models for time series and longitudinal data, and wait time models.In particular, magnetic resonance imaging (MRI) is an increasingly popular tool for measuring outcomes in MS clinical trials, but little research exists on the number of patients, number of scans per patient, and frequency of scanning required to detect the treatment effect of interest. Such information is critical given the high cost of MRI scans and the ethical issues surrounding the recruitment of patients to these trials.In addition, I am investigating a large, flexible class of models for data that are collected over time on one or more patients: parameter-driven models (PDMs). Characterizing such data is especially difficult if the response is a count or is binary. PDMs encompass several popular models, which can allow us to apply theory developed for one model to others in this class. I am investigating whether simple estimation methods for one such model (the hidden Markov model) can be used to obtain approximate estimators of more complex PDMs.Finally, I am interested in the analysis of survival and wait time data. I am proposing two different projects in this field. The first involves the modelling of wait times for hip, knee, and cataract surgery in British Columbia, with the goal of developing an optimal scheduling policy. These data are challenging to describe due to non-random sampling of patients appearing on the wait list and non-random drop-offs from this list. The second concerns the estimation of the effects of predictor variables when survival times are discrete and the sample size is small.

My research is largely motivated by medical applications, and involves three broad areas of statistics: designs for multiple sclerosis (MS) clinical trials, models for time series and longitudinal data, and wait time models.In particular, magnetic resonance imaging (MRI) is an increasingly popular tool for measuring outcomes in MS clinical trials, but little research exists on the number of patients, number of scans per patient, and frequency of scanning required to detect the treatment effect of 兴趣。鉴于MRI扫描的高成本以及围绕患者招募参加这些试验的伦理问题，此类信息至关重要。此外，我正在研究一类大型，灵活的数据类别，这些模型会随着时间的流逝而在一个或多个患者上收集：参数驱动的模型（PDMS）。如果响应是计数或二进制，则表征此类数据特别困难。 PDM涵盖了几种流行的模型，这些模型可以使我们能够将开发的一个模型的理论应用于此类的其他模型。我正在研究是否可以使用一个此类模型的简单估计方法（隐藏的马尔可夫模型）来获得更复杂的PDM的近似估计器。从本文中，我对生存和等待时间数据的分析感兴趣。我建议在该领域的两个不同的项目。首先涉及在不列颠哥伦比亚省的髋关节，膝盖和白内障手术的等待时间建模，目的是制定最佳的调度政策。由于对等待名单上出现的患者的非随机抽样以及此列表中的非随机脱落的患者的非随机抽样，这些数据是具有挑战性的。第二个涉及到生存时间离散并且样本量较小时预测变量效应的估计。