Center for Observational Research, Amgen Inc., Thousand Oaks, CA 91320, USA.
Adv Chronic Kidney Dis. 2012 Jan;19(1):19-26. doi: 10.1053/j.ackd.2012.01.001.
Confounding is an important source of bias in nonexperimental studies, arising when the effect of an exposure on the occurrence of an outcome is distorted by the effect of some other factor. In nonexperimental studies of patients with CKD or who are on chronic dialysis, confounding is a significant concern owing to the high burden of comorbid disease, extent of required clinical management, and high frequency of adverse clinical events in this patient population. Confounding can be addressed in both the design stage (restriction, accurate measurement of confounders) and analysis stage (stratification, multivariable adjustment, propensity scores, marginal structural models, instrumental variable) of a study. Time-dependent confounding and confounding by indication are 2 special cases of confounding that can arise in studies of treatment effects and may require more sophisticated analytic techniques to adequately address. The availability and expanded use of large health care databases have ensured greater precision and have now placed the focus on validity. Addressing the major threats to validity, such as confounding, should be a first-order concern.
混杂是指在非实验性研究中，由于其他因素的影响，暴露对结局发生的效应发生扭曲，从而导致偏倚的一个重要来源。在患有 CKD 或正在接受慢性透析的患者的非实验性研究中，由于合并症负担重、所需临床管理程度高以及该患者人群中不良临床事件的发生频率高，混杂是一个重大问题。混杂可以在研究的设计阶段（限制、混杂因素的准确测量）和分析阶段（分层、多变量调整、倾向评分、边缘结构模型、工具变量）进行处理。时间依赖性混杂和依指征混杂是治疗效果研究中可能出现的 2 种特殊混杂情况，可能需要更复杂的分析技术来充分解决。大型医疗保健数据库的可用性和广泛使用确保了更高的精确性，现在关注点已经转移到有效性上。解决有效性的主要威胁，如混杂，应该是首要关注的问题。
