Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
Institute of Statistics, Ulm University, Ulm, Germany.
Antimicrob Agents Chemother. 2017 Dec 21;62(1). doi: 10.1128/AAC.01691-17. Print 2018 Jan.
In current and former clinical trials for the development of antibacterial drugs, various primary endpoints have been used, and treatment effects are evaluated mostly in noninferiority analyses at the end of follow-up, which varies between studies. A more convincing and highly patient-relevant statement would be a noninferiority assessment over the entire follow-up period with cure and death as coprimary endpoints, while preserving the desired alpha level for statistical testing. To account for the time-dynamic pattern of cure and death, we apply a cure-death multistate model. The endpoint of interest is "get cured and stay alive over time." Noninferiority between treatments over the entire follow-up period is studied by means of one-sided confidence bands provided by a flexible resampling technique. We illustrate the technique by applying it to a recently published study and establish noninferiority in being cured and alive over a time frame of interest for the entire population, patients with hospital-acquired pneumonia, but not for the subset of patients with ventilator-associated pneumonia. Our analysis improves the original results in the sense that our endpoint is more patient benefiting, a stronger noninferiority statement is demonstrated, and the time dependency of cure and death, competing events, and different follow-up times is captured. Multistate methodology combined with confidence bands adds a valuable statistical tool for clinical trials in the context of infection control. The framework is not restricted to the cure-death model but can be adapted to more complex multistate endpoints and equivalence or superiority analyses.
在当前和以前开发抗菌药物的临床试验中,使用了各种主要终点,并且治疗效果主要在随访结束时的非劣效性分析中进行评估,而不同的研究随访时间不同。更有说服力和更能反映患者实际情况的说法是,在整个随访期间以治愈和死亡为共同主要终点进行非劣效性评估,同时保留用于统计检验的期望α水平。为了考虑治愈和死亡的时间动态模式,我们应用了治愈-死亡多状态模型。感兴趣的终点是“随着时间的推移治愈并保持存活”。通过使用灵活的重抽样技术提供的单侧置信带,研究整个随访期间治疗之间的非劣效性。我们通过将其应用于最近发表的一项研究来说明该技术,并在整个研究人群、医院获得性肺炎患者的感兴趣时间段内建立治疗的非劣效性,但对于呼吸机相关性肺炎患者亚组则不成立。我们的分析改进了原始结果,因为我们的终点更有利于患者,证明了更强的非劣效性声明,并且捕捉到了治愈和死亡、竞争事件和不同随访时间的时间依赖性。多状态方法结合置信带为感染控制背景下的临床试验增加了有价值的统计工具。该框架不仅限于治愈-死亡模型,还可以适用于更复杂的多状态终点和等效性或优效性分析。