Biostatistics: Design and Analysis of Experimental Studies; and Evaluating Classifier Tests.

生物统计学：实验研究的设计和分析；

• 批准号: RGPIN-2016-03670
• 负责人: Walter, Stephen
• 金额: $ 1.6万
• 依托单位: McMaster 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=668033
• 关键词: Biostatistics; Design; Analysis; Experimental; Studies

This project will address estimation problems in two related areas of biostatistics: Design and Analysis of Experimental Studies, and Evaluating Classifier Tests. Both topics require the development of innovative statistical models.****Topic #1: In this initiative, the applicant is considering the impact of preference effects in randomised experiments. Study outcomes may be affected by 1) preferences held by participants for the alternative interventions (inducing a selection effect); and 2) whether participants actually receive their preferred intervention (a preference effect). These effects cannot be estimated in a conventional experiment (where only the intervention effect is identifiable), but one can consider an alternative – the two-stage randomised design. The applicant has developed an analytic model that uses two-stage data from participants who are either randomised or who choose their intervention, in order to estimate all these effects, and has shown how design efficiency can be maximised. Current objectives are to enhance the two-stage design, to allow for participants who cannot decide between interventions. Longer term objectives include optimising design efficiency, and quantifying the required sample size and study power for the enhanced design.***Topic #2: While experimental designs are sometimes used, evaluation of classifier tests often requires other approaches. Classifiers are used to distinguish items with or without some attribute. There is often no error-free test available, so an imperfect reference standard must be used instead, subject to error. The applicant has developed latent class models to assess classifier accuracy in this situation, including models to evaluate tests for genetic polymorphisms, and to compare international biochemistry labs.****More recently the applicant has considered the impact of screening on prostate cancer mortality. A European study has shown a 21% mortality reduction, but a similar study in the USA showed no benefit. This has led to inconsistencies in recommendations about screening, which is now of major scientific interest. Adjudication of causes of death is subjective, and there is no gold standard. The European investigators therefore challenged the applicant to formulate an innovative model to assess adjudicator accuracy, and subsequently to correct the study results for bias due to measurement error. A longer term objective is to develop methods to estimate the rate of screening “over-detection”, this being the identification of non-lethal cancers. ***Both of these areas have been investigated by the applicant for some time, and form part of his long term research agenda. They are also very suitable areas for graduate students in statistics or biostatistics, disciplines for which there is currently a shortage of HQP in Canada. As such, training of HQP in these areas will be addressing a clear need in the Canadian context. ******

这个项目将解决生物统计学两个相关领域的估计问题：实验研究的设计和分析，以及评估分类器测试。这两个主题都需要开发创新的统计模型。主题#1：在这项倡议中，申请人正在考虑随机实验中偏好效应的影响。研究结果可能会受到以下因素的影响：1）参与者对替代干预措施的偏好（诱导选择效应）; 2）参与者是否实际接受了他们偏好的干预措施（偏好效应）。这些影响无法在传统实验中估计（只有干预效果是可识别的），但可以考虑一种替代方案-两阶段随机设计。 申请人开发了一种分析模型，该模型使用来自随机化或选择干预措施的参与者的两阶段数据，以估计所有这些影响，并展示了如何最大限度地提高设计效率。目前的目标是加强两阶段设计，使无法在干预措施之间做出决定的参与者能够参与。长期目标包括优化设计效率，量化增强设计所需的样本量和研究功效。*主题#2：虽然有时使用实验设计，但分类器测试的评估通常需要其他方法。分类器用于区分具有或不具有某些属性的项目。通常没有无误差的测试，因此必须使用不完美的参比标准品来代替，可能会出现错误。申请人已经开发了潜在类别模型来评估这种情况下的分类器准确性，包括评估遗传多态性测试和比较国际生物化学实验室的模型。最近，申请人考虑了筛查对前列腺癌死亡率的影响。欧洲的一项研究显示，死亡率降低了21%，但美国的一项类似研究显示没有任何好处。这导致了关于筛查的建议不一致，这是现在的主要科学兴趣。死亡原因的判定是主观的，没有金标准。因此，欧洲研究者要求申请人制定一个创新模型来评估裁定者的准确性，并随后纠正研究结果中因测量误差而产生的偏倚。一个长期目标是开发方法来估计筛查“过度检测”的比率，这是对非致命癌症的识别。* 申请人对这两个领域的研究已经有一段时间了，并构成其长期研究议程的一部分。它们也非常适合统计学或生物统计学的研究生，这些学科目前在加拿大缺乏HQP。因此，在这些领域对人力资源合格人员的培训将满足加拿大的明确需要。******