Data Management and Biostastics Core

这个数据管理和生物统计核心将支持和加强关于利什曼病的化疗、免疫学和生态学的项目。化疗项目作为一项随机临床试验，在数据管理和分析方面要求更为严格，因此受到更大的重视。总体目标是确保CIDEIM（主要外国合作者）的数据管理和生物统计能力达到高国际标准，并具有可持续性。相对于生物统计学，目前的数据管理能力较低，需要进一步加强。为实现总目标，我们制定了五个具体目标，并规划了以下活动：1)在拟进行的数据管理中，达到所要求的准确性和安全性标准

This Data Management and Biostatistics Core will support and strengthen projects on the chemotherapy, immunology, and ecology of leishmaniasis. As a randomized clinical trial, the chemotherapy project has more stringent requirements in terms of data management and analysis, so receives greater emphasis. The general objective is to ensure that the data management and biostatistics capacities of CIDEIM (the Major Foreign Collaborator) reach high international standards, and are sustainable. Relative to biostatistics, the current data management capacity is lower, so requires more strengthening. In order to reach the general objective, we have set five specific aims, and planned the following activities. 1) To attain the required standards of accuracy and security in the data management of the proposed projects. We will develop custom data management systems using the TrialDB package developed in Yale, 2) To enable CIDEIM to sustain these standards for future projects. CIDEIM personnel will be trained in TrialDB, and will run the custom systems when they have been transferred from Yale to CIDEIM. 3) To attain the required standards in statistical aspects of design, planning and execution of the proposed intervention trial. Experienced statisticians at CIDEIM and Yale have, together with the trial investigators, constructed the trial design to give adequate statistical power; to minimize bias; and, by means of a planned interim analysis, to allow for early stopping. 4) To devise and implement a Data and Safety Monitoring Plan, and convene a Data Safety and Monitoring Board (DSMB). Although the trial is Phase II, rather than III, due to the multisite nature of the trial, and inclusion of children, we have decided to convene a DSMB to monitor progress and advise the investigators. 5) To devise and execute a plan for statistical analysis of the trial. The trial has a well defined binary endpoint, and the planned analysis will yields valid statistical inference, taking into account factors such as the prior interim analysis, and the possible need to use exact, rather than asymptotic, logistic regression.