论文标题
风险比回归 - 简单概念但复杂的计算
Risk Ratio regression -- simple concept yet complex computation
论文作者
论文摘要
风险比（RR）是暴露在未暴露的结果风险中的结果之比。这是一个简单的概念，这使人们感到奇怪，为什么它没有获得与优势比相同的受欢迎程度。在流行病学中，使用逻辑回归来估计优势比非常普遍，并将优势比解释为风险比，这也很常见。一方面，估计优势比很简单，但解释很难。另一方面，估计风险比是具有挑战性的，但其解释很简单。四十年后，估计风险比的问题仍然存在。这些问题包括算法的收敛性，回归规范的选择（例如对数二元，泊松）等。可以使用各种新的计算方法，有助于克服收敛问题，并提供RR的双重稳健估计。
The Risk Ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remains after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available that help overcome the issue of convergence and provide doubly robust estimates of RR.
