Pooling HIV Cohorts to Inform Clinical Decision

描述（由申请方提供）：我们建议汇总5项HIV队列研究的数据，以回答HIV感染患者管理中的关键问题：何时开始高效抗逆转录病毒治疗（HAART）？，哪种HAART方案作为初始治疗效果更好？什么时候换到另一个HAART方案？观察性队列研究适合于这种分析，因为它们比随机试验更具成本效益，使用比随机试验更少限制性的合格标准，并且当进行适当分析时，分析提供的HAART疗效估计接近随机试验。然而，由于样本量的考虑，任何单一队列不太可能足以获得稳定的估计值。因此，我们汇总了西欧和北美五项正在进行的前瞻性队列研究的数据：退伍军人老龄化队列研究（美国），瑞士HIV队列研究（瑞士），法国医院数据库HIV研究（法国），GEMES（西班牙）和PISCIS（加泰罗尼亚和巴利阿里群岛，西班牙）。这些研究总共招募了14万多名艾滋病毒感染者。我们将以尽可能接近随机试验设计的方式使用来自几个观察性队列的数据。然后，我们将应用逆概率加权、g估计和参数g公式（由Robins及其合作者在过去二十年中开发的方法）。这些方法不仅在理论上有效地解决了这些问题，而且在应用于观察数据时也成功地再现了以前随机试验的结果。我们的研究将为循证指南和临床试验计划提供信息。此外，这种先进方法的成功应用将促进美国和欧洲领先的艾滋病毒观察研究小组对因果建模的理解和培训。

DESCRIPTION (provided by applicant): We propose to pool data from five HIV cohort studies to answer pivotal questions in the management of patients with HIV infection: when to initiate Highly Active Antiretroviral Therapy (HAART)?, which HAART regimes work better as initial treatment?, and when to switch to another HAART regime? Observational cohort studies are appropriate for this analyses as they are more cost-efficient than randomized trials, use less restrictive eligibility criteria than randomized trials, and, when analyzed appropriately analyzed have provided estimates of the efficacy of HAART close to those obtained from the randomized trials. Nevertheless, because of sample size considerations, it is unlikely that any single cohort will be sufficient to obtain stable estimates. We therefore pool data from five ongoing prospective cohort studies in Western Europe and North America: the Veterans Aging Cohort Study (USA), the Swiss HIV Cohort Study (Switzerland), the French Hospital Database HIV Study (France), GEMES (Spain), and PISCIS (Catalonia and Balearic Islands, Spain). Together, these studies have recruited more than 140,000 HIV infected patients. We will use the data from several observational cohorts in a way that mimics the design of randomized trials as closely as possible. We will then apply inverse probability weighting, g-estimation, and the parametric g- formula (methods developed by Robins and collaborators over the last two decades). These methods are not only theoretically valid to address these questions but have also successfully reproduced results from previous randomized trials when applied to observational data. Our study will inform evidence-based guidelines, and the planning of clinical trials. In addition, the successful application of this advanced methodology as proposed will facilitate understanding and training in causal modeling across leading HIV observational research groups in the United States and Europe.