Hauck W W, Anderson S, Marcus S M
Division of Clinical Pharmacology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, USA.
Control Clin Trials. 1998 Jun;19(3):249-56. doi: 10.1016/s0197-2456(97)00147-5.
The analyses of the primary objectives of randomized clinical trials often are not adjusted for covariates, except possibly for stratification variables. For analyses with linear models, adjustment is a precision issue only. We review the literature regarding logistic and Cox (proportional hazards) regression models. For these nonlinear analyses, omitting covariates from the analysis of randomized trials leads to a loss of efficiency as well as a change in the treatment effect being estimated. We recommend that the primary analyses adjust for important prognostic covariates in order to come as close as possible to the clinically most relevant subject-specific measure of treatment effect. Additional benefits would be an increase in efficiency of tests for no treatment effect and improved external validity. The latter is particularly relevant to meta-analyses.
除了可能的分层变量外，随机临床试验主要目标的分析通常不对协变量进行调整。对于线性模型分析，调整只是一个精度问题。我们回顾了关于逻辑回归和Cox（比例风险）回归模型的文献。对于这些非线性分析，在随机试验分析中忽略协变量会导致效率损失以及所估计的治疗效果发生变化。我们建议主要分析应对重要的预后协变量进行调整，以便尽可能接近临床上最相关的个体特异性治疗效果测量值。额外的好处是无治疗效果检验的效率提高以及外部有效性得到改善。后者对于荟萃分析尤为重要。
