Should we adjust for covariates in nonlinear regression analyses of randomized trials?
Should we adjust for covariates in nonlinear regression analyses of randomized trials?
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DOI：
10.1016/s0197-2456(97)00147-5
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发表时间：
1998-06-01
期刊：
影响因子：
--
通讯作者：
Marcus, SM
中科院分区：
文献类型：
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作者：
Hauck, WW;Anderson, S;Marcus, SM
随机临床试验的主要目标分析通常不考虑协变量，除非可能考虑分层变量。对于线性模型的分析，平差只是一个精度问题。我们回顾了有关logistic和Cox（比例风险）回归模型的文献。对于这些非线性分析，从随机试验的分析中省略协变量会导致效率的损失以及被估计的治疗效果的变化。我们建议对重要的预后协变量进行初步分析调整，以便尽可能接近临床最相关的治疗效果的受试者特异性测量。额外的好处是提高无治疗效果测试的效率和提高外部效度。后者与元分析尤其相关。(C) Elsevier Science Inc., 1998。
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. (C) Elsevier Science Inc. 1998.