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首页> 外文期刊>BMC Medical Research Methodology >Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study
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Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study

机译：估计常见二元结果的相对风险时，稳健的泊松模型和对数二项式模型之间的异常值的鲁棒性比较：模拟研究

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Background 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. Methods In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. Results 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. Conclusions 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.
机译：背景技术为了估计常见二元结果的相对风险或风险比，最流行的基于模型的方法是稳健的（也称为修正的）泊松和对数二项式回归。在这两种方法中，由于对数二项回归基于最大似然，因此认为对数二项式回归可产生更有效的估计量，而健壮的Poisson模型可能不受异常值的影响。与对数二项式模型相比，支持鲁棒泊松模型的鲁棒性的证据非常有限。方法在本研究中，进行了仿真以评估两种方法在存在异常值的几种情况下的性能。结果结果表明，对于来自结果与协变量之间的关系为简单形式（例如对数线性）的总体的数据，这两个模型产生了可比的偏差和均方误差。但是，如果真实关系包含较高阶项，则即使污染程度较低，鲁棒的Poisson模型也始终优于对数二项式模型。结论在估计常见二元结果的相对风险或风险比时，健壮的Poisson模型与离群二项式模型相比对异常值更健壮（或更不敏感）。用户在选择合适的模型来估计相对风险或风险比率时应注意这些限制。

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