miR 92/19 cluster in the ERK context

ERK 背景下的 miR 92/19 簇

• 批准号: 10433819
• 负责人: Yajaira Suarez
• 金额: $ 52.01万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-10 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10433819
• 关键词: Molecular

The Tsepamo birth outcomes surveillance study has accrued many thousands of women living with HIV from Botswana. The Tsepamo Plus study (Project 1) will continue to collect ARV exposures in pregnancy which are rarely studied with high precision. With such large samples, Tsepamo can detect differences in rare pregnancy outcomes such as neural tube defects, and can provide precise estimates of effects for common outcomes such as prematurity. Because Tsepamo can provide high precision when there is much uncertainty about safety in pregnancy and Botswana typically rolls out new ARVs before other African countries, Tsepamo may be the first to report on novel safety signals. This in turn makes Tsepamo highly influential in the understanding of adverse ARV effects in pregnancy. A natural question is if first and subsequent analyses should apply group sequential statistical methodology to plan for “when to look” and to enhance the understanding of uncertainty. Further, guidance is sorely needed for understanding uncertainty and interpreting safety signals for unplanned analyses. Moreover, surveillance data alone describes circumstances as they are and are not directly set up to inform personal, clinical, or societal decision-making. For example, while a surveillance database may help detect an increase in neural tube defects, the data alone do not inform the optimal decision about what’s to be done to reduce neural tube defects in future pregnancies. Ideally, such decisions would be informed by randomized trials, but trials are often too costly, unethical, or not timely enough. Instead, we can only empirically inform decisions by emulating target trials using observational data. The principle behind target trial emulation is simple: lacking a randomized trial for a given research question, we describe in detail the protocol of the randomized trial we would like to conduct, and then combine subject matter expertise and appropriate statistical analyses to emulate that hypothetical trial using observational data. By embracing this framework, many of the common pitfalls of current observational research practices can be avoided; however, target trial emulation requires multidisciplinary collaboration and often novel analytic approaches. The objectives of this proposal are to evaluate methodologies and provide guidance on methods for making best use of Tsepamo data and similar studies. Specifically, to evaluate the use of group sequential methodology for surveillance systems in studies of pregnancy and to create guidance on statistical adjustments for unplanned analyses we have created the following aims: Aim 1: To create guidance on when to publicly report on safety signals based on unplanned analyses. We will compare the statistical properties of group sequential methods from Aim 1 to fixed sample methods which were used in the NTD example, and provide recommendations on the timing of public release of unplanned analyses. Considerations will include the probability of decision reversal, the probability of missing a true signal, and a comprehensive scientific understanding of the range of true effects consistent with the analysis. Aim 2: To evaluate the statistical properties (e.g. Power, Type one error) of various group sequential methods (e.g. Lan-DeMets error spending approach) when applied in the setting of large sample sizes with rare and common pregnancy outcomes. We will compare numerous approaches to account for tests of accumulating surveillance data in the Tsepamo study, and develop methods to account for uncertainty in the maximal number of women taking specific ART regimens. To further develop methods for meaningful and valid causal inferences in Tsepamo and related databases, and develop a sustainable program for target trial emulation in Botswana, we also aim: Aim 3: To develop and implement methods to benchmark an emulated target trial using Tsepamo data with results from a randomized trial. We will benchmark Tsepamo analyses against the VESTED trial to validate and calibrate our emulation approach, and then expand upon the VESTED trial findings to estimate effects on rare (e.g. stillbirth) and new (e.g. weight gain) outcomes and within subgroups (e.g. by maternal nutrition status) that the original trial was not able to assess. Aim 4: To develop and improve triangulation strategies for emulating target trials within pregnancyrelated surveillance data. We will develop guidelines, procedures, and software to support triangulating evidence, including integrating methods for transporting results from a randomized trial to Tsepamo’s underlying study population, as well as non-conventional study designs based on multiple pregnancies, proposed instrumental variables, and negative controls.

特塞帕莫出生结果监测研究积累了数以千计的艾滋病毒携带者 博茨瓦纳。TSepamo Plus研究(项目1)将继续收集怀孕期间接触ARV的情况 很少有高精度的研究。在如此大的样本下，TSepamo可以检测出罕见妊娠的差异 结果，如神经管缺陷，并可以提供对常见结果的影响的准确估计，如 是早产儿。因为当存在很大的安全性不确定性时，TSepamo可以提供高精度 怀孕和博茨瓦纳通常在其他非洲国家之前推出新的抗逆转录病毒药物，特塞帕莫可能是第一个 报道新的安全信号。这反过来又使特塞帕莫在理解不利因素方面产生了很大的影响 ARV对妊娠的影响。一个自然的问题是，第一次和随后的分析是否应该应用分组序贯 统计方法，以计划“何时查看”，并加强对不确定性的理解。此外， 为了理解不确定性和解释计划外分析的安全信号，迫切需要指导。 此外，监测数据本身描述了情况，而不是直接建立来告知 个人、临床或社会决策。例如，虽然监控数据库可能有助于检测 神经管缺陷的增加，数据本身并不能提供关于采取什么措施的最佳决策 在未来的怀孕中减少神经管缺陷。理想情况下，这样的决定将通过随机试验来通知， 但试验往往过于昂贵、不道德或不够及时。相反，我们只能凭经验为决策提供信息 通过使用观测数据模拟目标试验。目标试验仿真背后的原则很简单：缺乏 对于给定的研究问题，我们详细描述了随机试验的方案 愿意进行，然后结合主题专业知识和适当的统计分析来效仿 这项假设试验使用的是观测数据。通过采用此框架，许多常见的陷阱 目前的观察性研究实践是可以避免的；然而，目标试验仿真需要 多学科协作，往往是新颖的分析方法。 本提案的目标是对方法进行评估，并提供最佳方法的指导 使用特塞帕莫的数据和类似的研究。具体地说，为了评估群体序贯方法的使用情况 在怀孕研究中建立监测系统，并就意外情况的统计调整提供指导 分析我们创建了以下目标： 目标1：根据计划外分析，制定何时公开报告安全信号的指导方针。 我们将比较从目标1到固定样本方法的分组序贯方法的统计性质 在NTD示例中使用，并就计划外发布的时间提供建议 分析。考虑的因素将包括判决逆转的概率、错过真实信号的概率、 以及对与分析一致的真实效果范围的全面科学理解。 目标2：评估不同组序列的统计特性(例如，幂、一类错误 方法(如Lan-Demets误差支出法)在大样本容量设置中的应用 有罕见和常见的妊娠结局。我们将比较多种方法来说明以下测试 在TSepamo研究中积累监测数据，并开发方法来解释 接受特定抗逆转录病毒疗法的妇女人数最多。 进一步开发TSepamo和相关数据库中有意义和有效的因果推论的方法，以及 为了在博茨瓦纳制定可持续的目标试验仿真计划，我们的目标还包括： 目标3：开发和实施使用TSepamo数据对模拟目标试验进行基准测试的方法 来自随机试验的结果。我们将根据既得试验对TSepamo分析进行基准测试，以 验证和校准我们的仿真方法，然后扩展已有的试验结果以进行估计 对罕见的(如死产)和新的(如体重增加)结局的影响以及亚组内的影响(如按产妇 营养状况)，最初的试验无法评估。 目标4：开发和改进用于模拟妊娠相关目标试验的三角测量策略 监控数据。我们将开发支持三角测量的指南、程序和软件 证据，包括将随机试验的结果传输到TSepamo的基础上的整合方法 研究人群以及基于多胎妊娠的非常规研究设计 工具性变量和负性对照。