COBRE: USC: BIOMETRY CORE, COLON CANCER

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心机构，不一定为研究者机构。结肠癌研究中心的生物统计学核心参与了研究的设计、实验数据的分析，并为COBRE人员的研究补助金提案的统计组成部分的撰写提供了帮助。这包括：（1）就随机化、样本量和分析结果数据的统计方法等问题提供适当的研究设计建议;（2）开发适当的方法来确定适当的样本量，以达到针对特定情况的给定显著性水平的预定功效，这是现有方法所不涵盖的;（3）发展离散数据分析的统计方法（4）采用生存分析方法分析不完整或右删失数据;（5）发展纵向标记与事件时间或复发事件相关的分析模型和方法。在即将到来的一年里，生物统计学核心人员除了可供CCCR人员咨询和对实验数据进行统计分析外，还将参与开发新的统计方法，如适当的样本量确定技术，分析标记数据和事件时间数据的工具。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The Biometry Core of the Center for Colon Cancer Research has been involved in the designing of studies, the analysis of experimental data, and in providing assistance in the write-up of the statistical components for research grant proposals for COBRE personnel. This involves (1) giving advice on the proper design of studies in regards to issues of randomization, sample size, and the statistical methods of analyzing the resulting data; (2) developing appropriate methods for determining the appropriate sample sizes to achieve a prespecified power for a given level of significance that is tailored to the specific situation at hand, which is not covered by existing methods; (3) developing statistical methods of analysis of discrete data (tumor counts) with small sample sizes which is more appropriate for analyzing tumor count data of mice arising from the studies of CCCR personnel; (4) employing survival analytic methods to the analysis of data with which are incomplete or right-censored; (5) developing models and methods of analysis for relating longitudinal markers and event time or recurrent events. For the upcoming year, Biometry Core personnel, aside from being available for consultation by CCCR personnel and for performing the statistical analysis of experimental data, will also be involved in developing new statistical methods such as appropriate sample size determination techniques, tools for analyzing marker data and event time data.