Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression
Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression
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DOI：
10.1016/j.jclinepi.2006.12.001
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发表时间：
2007-09-01
影响因子：
7.2
通讯作者：
Berlin, Jesse A.
中科院分区：
文献类型：
--
作者：
Localio, A. Russell;Margolis, David J.;Berlin, Jesse A.
目的：评估当结局常见时，从多变量二元回归估计相对风险及其置信区间的替代统计方法。研究设计和设置：我们在一项单中心研究中对两组假设的患者进行了模拟，无论是随机的还是队列的，并重新分析了一项已发表的观察性研究。关注的结果是相对风险估计的偏倚、95%置信区间的覆盖率和赤池信息标准。结果如下：根据模拟，当结果是常见的时，计算相对风险的置信区间的常用方法在典型应用中大大夸大了统计意义。逻辑回归以外的广义线性模型有时无法收敛，或产生超过1.0的估计风险。使用逻辑回归和自举回归的条件或边际标准化估计[0，I]范围内的风险和具有适当置信区间的相对风险。结论：特别是当结果很常见时，相对风险和置信区间很容易从多变量logistic回归中间接计算。相比之下，对数线性回归模型在结果很常见的情况下是有问题的。(c)2007爱思唯尔公司All rights reserved.
Objective: To assess alternative statistical methods for estimating relative risks and their confidence intervals from multivariable binary regression when outcomes are common. Study Design and Setting: We performed simulations on two hypothetical groups of patients in a single-center study, either randomized or cohort, and reanalyzed a published observational study. Outcomes of interest were the bias of relative risk estimates, coverage of 95% confidence intervals, and the Akaike information criterion. Results: According to simulations, a commonly used method of computing confidence intervals for relative risk substantially overstates statistical significance in typical applications when outcomes are common. Generalized linear models other than logistic regression sometimes failed to converge, or produced estimated risks that exceeded 1.0. Conditional or marginal standardization using logistic regression and bootstrap resampling estimated risks within the [0,I] bounds and relative risks with appropriate confidence intervals. Conclusion: Especially when outcomes are common, relative risks and confidence intervals are easily computed indirectly from multivariable logistic regression. Log-linear regression models, by contrast, are problematic when outcomes are common. (c) 2007 Elsevier Inc. All rights reserved.