Abstract：
Randomized trials can take more explanatory or more pragmatic approaches. Pragmatic studies, conducted closer to real-world conditions, assess treatment effectiveness while considering factors like protocol adherence. In these studies, intention-to-treat (ITT) analysis is fundamental, comparing outcomes regardless of the actual treatment received. Explanatory trials, conducted closer to optimal conditions, evaluate treatment efficacy, commonly with a per protocol (PP) analysis, which includes only outcomes from adherent participants. ITT and PP are strategies used in the conception, design, conduct (protocol execution), analysis, and interpretation of trials. Each serves distinct objectives. While both can be valid, when bias is controlled, and complementary, each has its own limitations. By excluding nonadherent participants, PP analyses can lose the benefits of randomization, resulting in group differences in factors (influencing adherence and outcomes) that were present at baseline. Additionally, clinical and social factors affecting adherence can also operate during follow-up, that is, after randomization. Therefore, incomplete adherence may introduce postrandomization confounding. Conversely, ITT analysis, including all participants regardless of adherence, may dilute treatment effects. Moreover, varying adherence levels could limit the applicability of ITT findings in settings with diverse adherence patterns. Both ITT and PP analyses can be affected by selection bias due to differential losses and nonresponse (ie, missing data) during follow-up. Combining high-quality and comprehensive data with advanced statistical methods, known as g-methods, like inverse probability weighting, may help address postrandomization confounding in PP analysis as well as selection bias in both ITT and PP analyses.
摘要：
随机试验可以采取更具解释性或更务实的方法。语用研究,进行得更接近真实世界的条件，评估治疗有效性，同时考虑方案依从性等因素。在这些研究中,意向治疗(ITT)分析是基础，比较结果，而不考虑实际接受的治疗。解释性试验，进行得更接近最优条件，评估治疗效果，通常与每个协议(PP)分析，其中仅包括来自坚持参与者的结果。ITT和PP是概念中使用的策略，设计,行为（协议执行），分析,和审判的解释。每个服务于不同的目标。虽然两者都可以有效，当偏置被控制时，互补,每个人都有自己的局限性。通过排除不坚持的参与者，PP分析可能会失去随机化的好处，导致基线时存在的因素(影响依从性和结局)的组间差异。此外,影响依从性的临床和社会因素也可以在随访期间起作用，即，随机化后。因此,不完全依从可能引入随机化后混淆.相反，ITT分析,包括所有参与者，无论是否坚持，可能会稀释治疗效果。此外，不同的依从性水平可能会限制ITT研究结果在具有不同依从性模式的环境中的适用性.ITT和PP分析都会受到由于差异损失和非响应而导致的选择偏差的影响(即，缺失数据)在随访期间。将高质量和全面的数据与先进的统计方法相结合，被称为G-方法，比如逆概率加权，可能有助于解决PP分析中的随机化混淆问题，以及ITT和PP分析中的选择偏差。
