Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo 113-0033, Japan.
Stat Med. 2010 Sep 10;29(20):2107-16. doi: 10.1002/sim.3987.
While intent-to-treat (ITT) analysis is widely accepted for superiority trials, there remains debate about its role in non-inferiority trials. It has often been said that ITT analysis tends to be anti-conservative in demonstrating non-inferiority, suggesting that per-protocol (PP) analysis may be preferable for non-inferiority trials, despite the inherent bias of such analyses. We propose using randomization-based g-estimation analyses that more effectively preserve the integrity of randomization than do the more widely used PP analyses. Simulation studies were conducted to investigate the impacts of different types of treatment changes on the conservatism or anti-conservatism of analyses using the ITT, PP, and g-estimation methods in a time-to-event outcome. The ITT results were anti-conservative for all simulations. Anti-conservativeness increased with the percentage of treatment change and was more pronounced for outcome-dependent treatment changes. PP analysis, in which treatment-switching cases were censored at the time of treatment change, maintained type I error near the nominal level for independent treatment changes, whereas for outcome-dependent cases, PP analysis was either conservative or anti-conservative depending on the mechanism underlying the percentage of treatment changes. G-estimation analysis maintained type I error near the nominal level even for outcome-dependent treatment changes, although information on unmeasured covariates is not used in the analysis. Thus, randomization-based g-estimation analyses should be used to supplement the more conventional ITT and PP analyses, especially for non-inferiority trials.
虽然意向治疗(ITT)分析被广泛用于优效性试验,但对于其在非劣效性试验中的作用仍存在争议。人们常说,ITT 分析在证明非劣效性时往往趋于保守,这表明尽管存在偏倚,但按方案(PP)分析可能更适合非劣效性试验。我们建议使用基于随机化的 g 估计分析,这种分析比更广泛使用的 PP 分析更有效地保持随机化的完整性。我们进行了模拟研究,以调查不同类型的治疗改变对使用 ITT、PP 和 g 估计方法在时间事件结局中分析的保守性或反保守性的影响。对于所有模拟,ITT 结果均为反保守性。反保守性随着治疗改变的百分比而增加,并且对于依赖结局的治疗改变更为明显。PP 分析将治疗转换病例在治疗改变时截尾,对于独立的治疗改变,保持了Ⅰ类错误接近名义水平,而对于依赖结局的病例,PP 分析是保守的还是反保守的,取决于治疗改变百分比的潜在机制。即使对于依赖结局的治疗改变,g 估计分析也能保持Ⅰ类错误接近名义水平,尽管分析中未使用未测量协变量的信息。因此,应使用基于随机化的 g 估计分析来补充更传统的 ITT 和 PP 分析,特别是对于非劣效性试验。