Methodology and design for biostatistics

生物统计学的方法和设计

• 批准号: 356107-2009
• 负责人: Atherton, Juli
• 金额: $ 1.31万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572741
• 关键词: Methodology; design; biostatistics

My research agenda concerns the areas of optimal and experimental design and survival analysis. In optimal design one determines where or when measurements should be taken in order to make the "best" inference possible from the data that will be collected. For the purpose of illustration, consider a clinical trial where 50 people are administered a blood pressure lowering treatment. Can we develop designs that will allow us to "best" estimate: (i) the proportion of people affected by the treatment, (ii) the timing of the treatment effect and/or, (iii) the before- and after-change average blood pressures. An interesting application of experimental design is in genomics. In the last 20 years many new technologies have been developed for studying our DNA and variations in our DNA that might suggest predisposition to diseases such as cancer. These experiments are often very costly hence their design is a key issue in ensuring that the best quality data possible can be collected. The area of survival analysis is concerned with observations of "lifetimes" that are incomplete in some way. For example, when following subjects from the onset of their disease some may be lost to follow-up. All that is known for such subjects is that they survived up until they were lost from the study. My proposed research in survival analysis is concerned with observed "lifetimes" that tend to be longer than expected because of the way the subjects were selected into the study group. Simply, longer survivors are observed, shorter ones are not, and adjustments in the analysis must be made to accommodate this selection bias. These adjustments help improve our inference and, hence, they have important implications when considering the natural history of a disease.

我的研究议程涉及最佳和实验设计和生存分析领域。在最优设计中，人们确定在何处或何时进行测量，以便从将要收集的数据中做出“最佳”推断。为了说明的目的，考虑一项临床试验，其中50人接受降压治疗。我们是否可以开发设计，使我们能够“最佳”估计：（i）受治疗影响的人群比例，（ii）治疗效果的时间和/或（iii）改变前后的平均血压。实验设计的一个有趣的应用是在基因组学中。在过去的20年里，许多新技术已经开发出来，用于研究我们的DNA和我们DNA中的变异，这些变异可能表明我们易患癌症等疾病。这些实验通常非常昂贵，因此它们的设计是确保可以收集最佳质量数据的关键问题。 生存分析领域关注的是对“生命期”的观察，这些观察在某种程度上是不完整的。例如，当从受试者的疾病发作开始对其进行随访时，一些受试者可能失访。对这些受试者所知的是，他们一直存活到研究结束。我提议的生存分析研究关注的是观察到的“寿命”，由于受试者被选入研究组的方式，“寿命”往往比预期的要长。简单地说，观察到的存活时间较长，而存活时间较短的则没有，因此必须对分析进行调整，以适应这种选择偏倚。这些调整有助于改善我们的推断，因此，在考虑疾病的自然史时，它们具有重要的意义。