Biostatistics

研究计划：视觉生物统计学模块 概述 视觉生物统计模块将为加州大学圣迭戈分校的视觉研究社区提供统计专业知识。到目前为止，每个调查员都独立地管理其数据和分析资源，经常重复资源和专业知识。此外，每个调查员都没有足够的能力获得足够的统计资源，以使他们的研究达到下一个生产力和复杂性的水平。视觉生物统计模块将为临床和实验研究的研究人员提供统计分析和咨询，以实现提高研究生产率和扩大加州大学圣迭戈分校视觉研究界的合作的共同目标。 这位高级生物统计师是第一位入驻眼科的高级生物统计师，他将通过为视力研究人员提供专门的统计咨询和分析来增强每位研究人员的研究能力。目前，每个调查员要么与外部统计学家建立合作关系，要么自己或通过学生和/或博士后研究员完成统计分析。最常见的情况是，咨询生物统计师的兼职能力非常有限，这限制了他/她深入了解特定视野的分析问题和跟上该领域最新进展的能力。有几个常见的分析主题和统计问题，可以通过让熟悉眼睛研究的专门生物统计学家来分析与视力相关的数据来有效和高效地解决。例如，可以使用统计学问题和新方法来解决对具有两只(非独立)眼睛的受试者/动物的纵向成像数据的分析。此外，使用这一模块的大多数赠款(包括人类和动物)都利用诸如光谱域光学相干断层扫描等成像设备来提供视网膜结构的测量。这些工具提供了大量相关的测量和汇总参数，必须用适当的统计模型进行有效分析。本模块中的生物统计员将熟悉这些摘要衡量标准和它们所代表的分析挑战，而这些标准又将成为使用这些技术的其他模块的参考资料。随着时间的推移，专业统计师对视觉研究数据的熟悉应会极大地提高生产率，因为这将省去向外部统计学家解释数据结构的耗时步骤。最后，利用基因、成像和功能数据来预测各种眼病的发展或进展涉及复杂的预测建模技术，这些技术在赠款中是相似的。

RESEARCH PLAN: VISION BIOSTATISTICS MODULE OVERVIEW The Vision Biostatistics Module will provide statistical expertise for the vision research community at UCSD. To date, each investigator has managed its data and analysis resources independently, often duplicating resources and expertise. Moreover, each investigator has not had adequate capability to acquire sufficient statistical resources to bring their research to the next level of productivity and sophistication. The Vision Biostatistics Module will provide statistical analyses and consultation to investigators of both clinical and experimental studies towards a common goal of improving the research productivity and expanding collaboration in the vision research community at UCSD. The staff senior biostatistician, the first to reside at the Department of Ophthalmology, will enhance the research capabilities of each investigator by providing dedicated statistical consultation and analyses for vision researchers. Currently, each investigator either has a collaborative relationship with an outside statistician or completes the statistical analysis himself or through students and/or postdoctoral fellows. Most often the consultant biostatistician is involved in a very limited part-time capacity, limiting his/her ability to develop an in-depth understanding of vision-specific analysis issues and to keep abreast of recent advancements in the field. There are several common analysis themes and statistical issues that can be addressed effectively and efficiently by having a dedicated biostatistician familiar with eye research to analyze vision related data. For example, there are statistical issues and novel methods that can be used to address the analysis of longitudinal imaging data of subjects/animals with two (non-independent) eyes contributing to the analysis. In addition, most of the grants (both human and animal) using this module utilize imaging instruments such as spectral domain optical coherence tomography to provide measures of retinal structure. These instruments provide a large amount of correlated measurements and summary parameters that must be analyzed efficiently with appropriate statistical models. The biostatistician in this module will become familiar with these summary measures and analytic challenges they represent which in turn will serve as a resource to other modules using these technologies. The familiarity of a dedicated statistician with vision research data should greatly enhance productivity over time as it will remove the time-consuming steps of explaining the data structure to outside statisticians. Finally, utilizing genetic, imaging and functional data to predict the development or progression of various eye diseases involves sophisticated prediction modeling techniques that are similar across grants.