Biostatistics, Data Management, and Bioinformatics Core

德克萨斯大学生物统计学、数据管理和生物信息学核心。安德森 癌症中心白血病孢子将是一个全面的，多边的数据采集资源， 管理，实验室实验和临床试验的设计，创新统计的发展 方法学、统计分析和发表通过白血病产生的转化研究 SPORE程序。 生物统计学，数据管理和生物信息学核心将纳入合理的实验设计 将使用适当的统计方法进行数据分析， 通过书面报告和与项目的频繁互动来解释结果 investigators.生物统计学、数据管理和生物信息学核心将提供一个综合数据库， 管理系统，以促进所有项目和核心之间的沟通，这将是定制的， 满足白血病科的需求。该过程包括前瞻性数据收集、数据 质量控制、数据安全和患者保密性。因此，从开始到报告， 实验将受益于SPORE资源，这些资源将用于增强现有的M。D.安德森 生物统计资源。 相关性（参见说明）： 在设计、实施、分析和解释方面进行有效的生物统计咨询和合作， 研究增加了这些研究实现其目标的可能性， 将得出结论。利用适当的生物统计模型和方法编制 研究报告和解释对于得出正确的结论和确保发表是必不可少的 在适当的期刊上发表结果。

The Biostatistics, Data Management, and Bioinformatics Core forthe University of Texas M.D. Anderson Cancer Center Leukemia SPORE will be a comprehensive, multilateral resource for data acquisition and management, design of laboratory experiments and clinical trials, development of innovative statistical methodology, statistical analysis, and publishing translational research generated through the Leukemia SPORE program. The Biostatistics, Data Management, and Bioinformatics Core will incorporate sound experimental design principles within all Projects, will carry out data analyses using appropriate statistical methodology, and will contribute to interpretation of results through written reports and frequent interaction with Project investigators. The Biostatistics, Data Management, and Bioinformatics Core will provide an integrated data management system to facilitate communication among all Projects and Cores, which will be customized to meet the needs of the Department of Leukemia. This process includes prospective data collection, data quality control, data security, and patient confidentiality. Thus, from inception to reporting, translational experiments will benefit from SPORE resources that will be used to augment existing M. D. Anderson biostatistics resources. RELEVANCE (See instructions): Effective biostatistical consultation and collaboration on the design, conduct, analysis, and interpretation of research studies increases the likelihood that such studies will achieve their objectives, and that valid conclusions will be drawn. Utilization of appropriate biostatistical models and methods in the preparation of reports and interpretation of studies is essential to reaching correct conclusions and to ensure the publication of results in appropriate journals.