miR 92/19 cluster in the ERK context

Tsepamo出生结果监测研究已经积累了数千名感染艾滋病毒的妇女， 波札那. Tsepamo Plus研究（项目1）将继续收集妊娠期ARV暴露情况， 很少有高精度的研究。有了如此大的样本，Tsepamo可以检测出罕见妊娠的差异 结果，如神经管缺陷，并可以提供精确的估计影响，为常见的结果， 早产由于Tsepamo可以在安全性存在很大不确定性时提供高精度， 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，Tsepamo可能是第一个 报告新的安全信号。这反过来又使Tsepamo在了解不利因素方面具有很大的影响力。 ARV对妊娠的影响一个自然的问题是，如果第一次和随后的分析应适用组序贯 统计方法，以规划“何时看”，并加强对不确定性的理解。此外，本发明还 迫切需要指导来理解不确定性和解释计划外分析的安全信号。 此外，监测数据本身描述了实际情况，而不是直接提供信息。 个人、临床或社会决策。例如，虽然监视数据库可以帮助检测 神经管缺陷的增加，数据本身并不能告知关于要做什么的最佳决策， 减少未来怀孕中的神经管缺陷。理想情况下，这些决定将由随机试验提供信息， 但审判往往费用太高、不道德或不够及时。相反，我们只能凭经验做出决定， 通过使用观察数据模拟目标试验。目标试验模拟背后的原则很简单：缺乏 一个给定的研究问题的随机试验，我们详细描述了随机试验的协议，我们 我想进行，然后结合联合收割机主题的专业知识和适当的统计分析，以模拟 使用观察数据的假设试验。通过采用这一框架， 目前的观察性研究实践是可以避免的;然而，目标试验模拟需要 多学科合作和通常新颖的分析方法。 本提案的目的是评估各种方法，并就如何最好地利用 使用Tsepamo数据和类似研究。具体而言，评估使用组序贯方法， 在妊娠研究中建立监测系统，并为计划外妊娠的统计调整提供指导。 我们制定了以下目标： 目标1：制定关于何时公开报告基于计划外分析的安全性信号的指南。 我们将比较目标1中的组序贯方法与固定样本方法的统计特性 这是用在新台币的例子，并提供建议的时间公开释放的计划外 分析。考虑因素将包括决策逆转的概率，错过真实信号的概率， 以及对与分析相一致的真实效应范围的全面科学理解。 目的2：评价各种成组序贯方法的统计特性（例如功效、第一类错误） 方法（例如Lan-DeMets误差消耗方法），当应用于大样本量设置时 罕见和常见的妊娠结局。我们将比较许多方法来解释测试， 在Tsepamo研究中积累监测数据，并制定方法来解释 最大限度地增加接受特定抗逆转录病毒疗法的妇女人数。 在Tsepamo和相关数据库中进一步开发有意义和有效的因果推理方法， 制定一个可持续的计划，在博茨瓦纳的目标试验仿真，我们还旨在： 目标3：开发和实施使用Tsepamo数据对模拟靶试验进行基准测试的方法 随机试验的结果。我们将根据VESTED试验对Tsepamo分析进行基准测试， 验证和校准我们的模拟方法，然后扩展VESTED试验结果，以估计 对罕见（如死产）和新发（如体重增加）结局以及亚组内（如按母体 营养状况），这是原始试验无法评估的。 目标4：开发和改进三角测量策略，以模拟妊娠相关试验中的目标试验 监视数据。我们将制定指导方针、程序和软件来支持三角测量 证据，包括将随机试验的结果传输到Tsepamo基础研究的整合方法。 研究人群以及基于多胎妊娠的非传统研究设计， 仪器变量和阴性对照。

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.