Department of Population Health, New York University Grossman School of Medicine, New York, USA.
Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, USA.
BMC Med Res Methodol. 2023 Jan 25;23(1):25. doi: 10.1186/s12874-022-01813-4.
Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques.
We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power.
The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions.
This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.
为了寻找针对 COVID-19 的有效疗法，已启动了多项临床试验。这些试验通常在大流行已达高峰的地区开展。因此，在几年内同一地点可能需要多次 COVID-19 疫情高峰，才能在单个试验中实现全部入组。这促使我们实时汇集正在进行的、暂停的、提前终止的或已完成的随机对照试验（RCT）的个体患者数据（IPD），以便在大流行危机期间尽快找到有效疗法。然而，跨试验汇集会给研究设计带来巨大的不确定性（例如，RCT 的数量和样本量可能事先未知）。我们试图开发一种通用的治疗效果评估模型，该模型考虑了这些不确定性，同时允许使用贝叶斯监测技术在整个研究过程中进行连续监测。
我们详细介绍了模型开发的挑战和解决方案，描述了使用广泛模拟来确定最终分析计划的过程。这包括建立先验分布假设、评估和改进不同研究组成方案下的模型收敛性，以及评估是否可以扩展模型以适应多中心 RCT 并评估治疗效果的异质性。此外，我们认识到我们需要评估模型的拟合优度，因此我们探索了一种使用后验预测检查的方法。最后，鉴于大流行背景下研究的紧迫性，我们致力于频繁监测数据以评估疗效，并制定了针对误报率和功效的贝叶斯监测规则。
主要结局为 11 点有序量表。我们提出了用于估计治疗效果的累积比例优势模型的操作特征。该模型可以在研究设计存在巨大不确定性的情况下估计治疗效果。我们研究了违反比例优势假设的程度，以确定模型的准确性。我们通过进行频繁的中期分析而不增加错误结论的概率，展示了贝叶斯监测方法的灵活性。
本文描述了一种使用模拟支持前瞻性 IPD 荟萃分析设计的可转化框架。
