Advances in Biostatistics: Estimators Based on Misspecified Models and Predictions of Survival Times

生物统计学的进展：基于错误指定模型和生存时间预测的估计器

• 批准号: RGPIN-2018-04304
• 负责人: Altman, Rachel
• 金额: $ 1.31万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=643827
• 关键词: Advances; Biostatistics; Estimators; Based; Misspecified

My proposed research program has two themes: The development and study of simple estimators of complex models, and the prediction of survival times. Such methods are of particular importance in biostatistics, and will have impact on subject areas including rainfall modelling, multiple sclerosis, and ovarian cancer.******With respect to the first theme, I plan to work in the context of time series of rainfall observations, which are complicated to describe because they take on both continuous and zero values, and are correlated over time. Estimation of the effects of factors that influence rainfall can thus be challenging. I intend to investigate the performance of an estimator that assumes that the observations are, in fact, uncorrelated. Earlier work with count data suggests that such an estimator could be well-behaved, efficient, and simpler to compute than previously considered estimators. I will evaluate the properties of this estimator, and will apply my results to the problem of estimating time trends and the Oceanic Niño Index (“El Niño”) effect on rainfall in Costa Rica. I also plan to work in the context of multiple sclerosis (MS) clinical trials where the outcome is the number of lesions detected using MRI scans of patients' brains over the course of the study period, and where patients were selected for lesion activity at baseline (a so-called “enrichment” study design). Previous authors propose a naive estimator of the effect of treatment that ignores the patient selection process. I will study the performance of an alternative estimator that can be computed with little effort, yet performs better than the naïve estimator. My work will allow for improved estimation of the treatment effect – and for better planning of sample sizes – in enriched MS trials. I will subsequently extend our methods to other types of responses collected in enriched trials.******My second theme concerns the prediction of recurrence and death times of ovarian cancer patients. Our initial work has shown that random survival forests are a useful tool for predicting either recurrence or death times; I plan to extend these methods to allow for their simultaneous prediction. In addition, I will develop prediction intervals for survival times that allow the quantification of our uncertainty about our predictions. One challenge is that the prediction of the survival time of a patient with a low expected survival time is inherently an easier problem than the prediction of the survival time of a patient with a high expected survival time. I thus intend to develop methods for identifying subgroups of patients for whom relatively precise predictions can be computed. Ultimately, my collaborators and I hope to develop an online decision support tool that patients and their caregivers can use to guide the management of ovarian cancer risks.

我提出的研究计划有两个主题：复杂模型的简单估计器的开发和研究，以及生存时间的预测，这些方法在生物统计学中特别重要，并将对降雨模型、多发性硬化症和癌症等学科领域产生影响。卵巢癌。********关于第一个主题，我计划在降雨观测时间序列的背景下进行工作，这些数据很难描述，因为它们同时呈现连续值和零值，并且随时间推移而相关估计影响降雨的因素的影响因此，我打算研究假设观测值实际上不相关的估计器的性能，早期对计数数据的研究表明，这样的估计器可能表现良好、高效且比以前更容易计算。我将评估该估计器的属性，并将我的结果应用于估计时间趋势和海洋尼诺指数（“厄尔尼诺”）对哥斯达黎加降雨的影响。多发性硬化症 (MS) 临床试验的背景，其中结果是在研究期间使用患者大脑 MRI 扫描检测到的病变数量，并且根据基线时的病变活动选择患者（所谓的“富集”） ”研究设计）。之前的作者提出了一种忽略患者选择过程的朴素估计器，该估计器可以轻松计算，但比朴素估计器表现更好。我的工作将有助于在丰富的 MS 试验中改进对治疗效果的估计，并更好地规划样本量。我随后会将我们的方法扩展到在丰富的试验中收集的其他类型的反应。******我的第二个主题涉及。我们的初步工作表明，随机生存森林是预测复发或死亡时间的有用工具；我计划扩展这些方法以允许同时预测。将制定生存时间的预测区间，以量化我们的不确定性我们的预测面临的一个挑战是，预测预期生存时间较低的患者的生存时间本质上是一个比预测预期生存时间较高的患者的生存时间更容易的问题，因此我打算开发一些方法。最终，我和我的合作者希望开发出一种在线决策支持工具，患者及其护理人员可以用它来指导卵巢癌风险的管理。