Berkowitz Seth A, Rudolph Kara E, Basu Sanjay
Division of General Medicine and Clinical Epidemiology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill (S.A.B.).
Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill (S.A.B.).
Circ Cardiovasc Qual Outcomes. 2019 Mar;12(3):e004907. doi: 10.1161/CIRCOUTCOMES.118.004907.
Recent multisite trials reveal striking heterogeneities in results between trial sites. These may be because of population differences indicating different treatment benefits among different types of participants or site anomalies, such as failures to adhere to study protocols that could negatively affect study validity. We sought to determine whether a new data analysis strategy-transportability methods-could suggest site anomalies not readily identified through standard methods.
We applied transportability methods to 2 large, multicenter cardiovascular disease treatment trials: the TOPCAT trial (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist; n=3445) comparing spironolactone to placebo for heart failure (for which site anomalies were suspected) and the ACCORD BP trial (Action to Control Cardiovascular Risk in Diabetes-Blood Pressure; n=4733) comparing intensive-to-standard blood pressure treatment (for which site anomalies were not suspected). The transportability methods give expected results by standardizing from one site to another using data on participant covariates. The difference between the expected and observed results was assessed using calibration tests to identify whether treatment-effect differences between sites could be explained by participant population characteristics. Standard regression methods did not detect heterogeneities in TOPCAT between Russia/Georgia study sites suspected of study protocol violations and sites in the Americas ( P=0.12 for difference in primary cardiovascular outcome; P=0.20 for difference in total mortality). The transportability methods, however, detected the difference between Russia/Georgia sites and sites in the Americas ( P<0.001) and found that measured participant characteristics did not explain the between-site discrepancies. The transport methods found no such discrepancies between sites in ACCORD BP, suggesting participant characteristics explained between-site differences.
Transportability methods may be superior to standard approaches for detecting anomalies within multicenter randomized trials and assist data monitoring boards to determine whether important treatment-effect heterogeneities can be attributed to participant differences or potentially to site performance differences requiring further investigation.
近期的多中心试验显示，各试验点之间的结果存在显著异质性。这可能是由于人群差异，表明不同类型参与者之间的治疗益处不同，也可能是试验点异常，例如未能遵守研究方案，这可能会对研究有效性产生负面影响。我们试图确定一种新的数据分析策略——可移植性方法——是否能够揭示通过标准方法不易发现的试验点异常。
我们将可移植性方法应用于两项大型多中心心血管疾病治疗试验：TOPCAT试验（醛固酮拮抗剂治疗射血分数保留的心力衰竭；n = 3445），比较螺内酯与安慰剂治疗心力衰竭（怀疑存在试验点异常），以及ACCORD BP试验（糖尿病患者控制心血管风险行动——血压；n = 4733），比较强化血压治疗与标准血压治疗（未怀疑存在试验点异常）。可移植性方法通过使用参与者协变量数据从一个试验点到另一个试验点进行标准化来得出预期结果。使用校准测试评估预期结果与观察结果之间的差异，以确定试验点之间的治疗效果差异是否可以由参与者人群特征来解释。标准回归方法未检测到TOPCAT试验中怀疑违反研究方案的俄罗斯/格鲁吉亚试验点与美洲试验点之间的异质性（主要心血管结局差异P = 0.12；总死亡率差异P = 0.20）。然而，可移植性方法检测到了俄罗斯/格鲁吉亚试验点与美洲试验点之间的差异（P < 0.001），并发现所测量的参与者特征无法解释试验点之间的差异。可移植性方法在ACCORD BP试验的试验点之间未发现此类差异，表明参与者特征解释了试验点之间的差异。
在检测多中心随机试验中的异常方面，可移植性方法可能优于标准方法，并有助于数据监测委员会确定重要的治疗效果异质性是否可归因于参与者差异，或潜在地归因于需要进一步调查的试验点表现差异。
