Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification.
Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification.
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DOI：
10.1186/s12874-018-0519-5
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发表时间：
2018-06-22
影响因子：
4
通讯作者：
Franklin M
中科院分区：
文献类型：
--
作者：
Chen W;Qian L;Shi J;Franklin M
对数二项模型和稳健(修正)泊松回归模型是估计二元反应变量风险比的常用方法。以前的研究表明，相比较而言，它们产生的点数估计和标准误差相似。然而，它们在模型错误指定情况下的表现却鲜为人知。在这项模拟研究中，比较了两种模型的统计性能，当LOG链接函数被错误指定或响应依赖于通过非线性关系(即截断响应)的预测器时。当连接函数被错误指定或响应变量的概率分布在右尾被截断时，来自对数二项模型的点估计是有偏差的。截断观测值的百分比与偏差的存在呈正相关，如果观测值来自响应率较低的人群，且被检查的其他参数是固定的，则偏差更大。相比之下，稳健泊松模型的点估计是公正的。在模型错误指定的情况下，稳健泊松模型通常更可取，因为它提供了对风险比率的无偏估计。本文的在线版本(10.1186/s12874-0180519-5)包含向授权用户提供的补充材料。
Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. The online version of this article (10.1186/s12874-018-0519-5) contains supplementary material, which is available to authorized users.
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