Chen Wansu, Shi Jiaxiao, Qian Lei, Azen Stanley P
Kaiser Permanente Southern California, Department of Research and Evaluation, Pasadena, CA, USA.
BMC Med Res Methodol. 2014 Jun 26;14:82. doi: 10.1186/1471-2288-14-82.
To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited.
In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed.
The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low.
The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.
为了估计常见二元结局的相对风险或风险比，最常用的基于模型的方法是稳健（也称为修正）泊松回归和对数二项回归。在这两种方法中，人们认为对数二项回归能产生更有效的估计量，因为它基于最大似然估计，而稳健泊松模型可能受异常值的影响较小。与对数二项模型相比，支持稳健泊松模型稳健性的证据非常有限。
在本研究中，进行了一项模拟，以评估这两种方法在存在异常值的几种情况下的性能。
研究结果表明，对于来自结局与协变量之间关系呈简单形式（例如对数线性）总体的数据，这两种模型产生的偏差和均方误差相当。然而，如果真实关系包含高阶项，即使污染水平较低，稳健泊松模型也始终优于对数二项模型。
在估计常见二元结局的相对风险或风险比时，与对数二项模型相比，稳健泊松模型对异常值更具稳健性（或敏感性更低）。在选择合适的模型来估计相对风险或风险比时，使用者应注意其局限性。
