Estimation of Risk Ratios in Cohort Studies With Common Outcomes A Bayesian Approach
Estimation of Risk Ratios in Cohort Studies With Common Outcomes A Bayesian Approach
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DOI：
10.1097/ede.0b013e3181f2012b
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发表时间：
2010-11-01
期刊：
影响因子：
5.4
通讯作者：
Cole, Stephen R.
中科院分区：
文献类型：
--
作者：
Chu, Haitao;Cole, Stephen R.
在具有共同结局的队列研究中，logistic回归分析估计的比值比通常被解释为风险比的间接估计。在这种情况下，比值比将比风险比更远离零。风险比的直接和无偏估计可通过最大似然法拟合的对数二项式模型获得。当最大似然对数二项模型无法收敛（常见）或提供预测概率估计值或置信上限大于1.0时，已经提出了各种方法，但如我们所述，每种方法都有缺点。我们提出了一种新的贝叶斯方法估计的风险比从对数二项模型，解决现有方法的缺点。后验计算可以很容易地完成使用WinBUG代码提供。
In cohort studies with common outcomes, the odds ratio estimated from a logistic regression analysis is often interpreted as an indirect estimate of the risk ratio. In such settings, the odds ratio will be farther from the null than the risk ratio. Direct and unbiased estimates of the risk ratio may be obtained from a log binomial model fit by maximum likelihood. When the maximum likelihood log binomial model fails to converge (as is common) or provides predicted probability estimates or upper confidence limits greater than 1.0, various approaches have been suggested, but each has drawbacks, as we describe. We propose a novel Bayesian approach for the estimation of the risk ratio from the log binomial model that addresses drawbacks of existing approaches. Posterior computation can be accomplished easily using the WinBUGs code provided.