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Vanderbilt-Nigeria Biostatistics Training Program (VN-BioStat)

Vanderbilt-Nigeria Biostatistics Training Program (VN-BioStat)
范德比尔特-尼日利亚生物统计学培训计划(VN-BioStat)
批准号:
10594548
负责人:
Muktar Hassan Aliyu
金额:
$29.77万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-04-01 至 2027-01-31

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中文摘要
翻译
项目摘要 在西非建立生物统计能力至关重要。西非的生物医学艾滋病毒研究正在增长,但 生物统计学的支持和专业知识正在落后。在西非和美国之间的艾滋病毒合作研究中。 机构,生物统计的领导者几乎总是来自美国。发展生物统计是很重要的 西非的领导者-生物统计学家,他们不是技术人员,而是联合调查员,负责人 研究人员和受资助的艾滋病毒/艾滋病研究的思想领袖。在中国有几个统计学博士项目 但它们与生物医学研究几乎或根本没有联系。资助的培训计划 在过去的十年里,在南部非洲和东非成功地建立了生物统计学,但没有 这样的项目目前存在于西非。 范德比尔特-尼日利亚生物统计学培训计划(VN-BioStat)建立在长期合作的基础上 在尼日利亚和美国两个领先的研究机构之间,即阿米努·卡诺教学 医院(AKTH)和范德比尔特大学医学中心(范德比尔特)建立了研究和培训 生物统计学家在尼日利亚进行艾滋病毒相关研究的平台。VN-BioStat将以以下资源为基础 AKTH及其母机构,卡诺的巴耶罗大学(BUK),创造了一批高技能的尼日利亚人 有能力领导和监督西非生物统计活动的生物统计学家。VN-生物状态 利用非常成功、协作和迅速扩展的美国国立研究院的投资组合 健康(NIH)和AKTH的其他资助研究,由范德比尔特调查人员共同领导。训练 尼日利亚生物统计学家的领导者,VN-BioStat计划建议每人带来两名尼日利亚数据科学家 为范德比尔特大学提供了为期一年的培训(5年共10次),让他们沉浸在生物统计学中,并接受实际操作培训。 在为期一年的培训期间,学员将领导设计、分析和发布一份协作性的 使用AKTH艾滋病毒数据的研究项目。他们还将领导一个相关的、注重方法的项目 这将在一份出版物中达到顶峰。在范德比尔特期间,他们将与一组艾滋病毒生物统计学家互动, 选修适当的生物统计学课程(每学期最多8学分,两学期),参加生物统计学 和全球卫生研讨会,参加赠款撰写研讨会,参加生物统计诊所,并合作 和其他尼日利亚调查人员。他们将得到范德比尔特学院的指导(一名生物统计学家和 一名临床研究员)和AKTH教员。我们还将在尼日利亚举办年度研讨会,为 对多达25名艾滋病毒研究人员进行生物统计学培训。潜在的课程主题包括要解决的方法 缺失数据,因果推理简介,回归建模策略,生物医学研究中的大数据, 和生存分析。
英文摘要
PROJECT ABSTRACT It is critical to build biostatistics capacity in West Africa. Biomedical HIV research is growing in West Africa, but biostatistical support and expertise are lagging. In collaborative HIV studies between West Africa and U.S. institutions, the biostatistics leaders are almost always from the U.S. It is important to develop biostatistics leaders in West Africa – biostatisticians who will not be technicians, but co-investigators, principal investigators, and thought-leaders in funded HIV/AIDS research. There are several statistics PhD programs in West Africa, but they often have little or no connection with biomedical research. Funded training initiatives over the past ten years have been successful at building biostatistics in Southern Africa and East Africa, but no such programs currently exist in West Africa. The Vanderbilt-Nigeria Biostatistics Training Program (VN-BioStat) builds on long-standing collaborations between two leading research institutions in Nigeria and the United States, namely, Aminu Kano Teaching Hospital (AKTH) and Vanderbilt University Medical Center (Vanderbilt) to establish a research and training platform for biostatisticians doing HIV-related research in Nigeria. VN-BioStat will build upon the resources of AKTH and its parent institution, Bayero University in Kano (BUK), to create a cohort of highly skilled Nigerian biostatisticians with the capacity to lead and supervise biostatistical activities in West Africa. VN-BioStat leverages the highly successful, collaborative and rapidly expanding portfolio of U.S. National Institutes of Health (NIH) and other funded research at AKTH, with joint leadership from Vanderbilt investigators. To train biostatistician leaders in Nigeria, the VN-BioStat program proposes to bring two Nigerian data scientists per year (total of 10 over 5 years) to Vanderbilt to immerse them in biostatistics and to receive hands on training. During the one-year training period, trainees will lead the design, analysis, and publication of a collaborative research project using HIV data from AKTH. They will also lead a related, methodologically focused project that will culminate in a publication. While at Vanderbilt, they will interact with a team of HIV biostatisticians, take appropriate biostatistics courses (up to 8 credit hours per semester for 2 semesters), attend biostatistics and global health seminars, participate in a grant-writing workshop, attend biostatistics clinics, and collaborate with other Nigerian investigators. They will receive mentorship from Vanderbilt faculty (one biostatistician and one clinical investigator) and AKTH faculty. We will also conduct annual workshops in Nigeria to provide mid- level biostatistics training for up to 25 HIV researchers. Potential course topics include methods to address missing data, introduction to causal inference, regression modeling strategies, big data in biomedical research, and survival analysis.
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