Global Biometric Sciences, Bristol-Myers Squibb, New Jersey, United States.
Translational Informatics, Sanofi, Bridgewater, New Jersey, United States.
Stat Med. 2018 Jun 30;37(14):2187-2207. doi: 10.1002/sim.7685. Epub 2018 Apr 17.
In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials.
在临床研究与开发中，中期监测对于更好地做出决策和尽量减少将患者暴露于可能无效疗法的风险至关重要。对于中期无效率或疗效监测，预测概率方法在实践中得到了广泛应用。这些方法已经针对单变量进行了很好的研究。然而，对于纵向研究，仅使用完成者的单变量信息的预测概率方法可能不是最有效的，并且可以利用正在进行的受试者的数据来提高效率。另一方面，利用正在进行的受试者的信息可以允许一旦有足够数量的受试者达到较早的时间点，就可以进行中期分析。对于纵向结果，我们推导出了预测概率的闭式公式，包括贝叶斯预测概率、预测能力和条件能力，还给出了未来试验中成功预测概率和最佳剂量成功预测概率的闭式解。当预测概率用于中期监测时，我们研究了它们的分布，并讨论了具有所需操作特性的分析截止值或停止边界。我们表明，与仅使用完成者信息相比，利用所有纵向信息的预测概率对于中期监测更为有效。为了说明它们在纵向数据中的实际应用，我们分析了来自临床试验的 2 个真实数据示例。
