Kaiser Permanente Southern California, Department of Research and Evaluation, 100 S. Los Robles Ave, 2nd Floor, Pasadena, CA, 91101, USA.
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, CA, 90033, USA.
BMC Med Res Methodol. 2018 Jun 22;18(1):63. doi: 10.1186/s12874-018-0519-5.
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.
对数二项式和稳健（修正）泊松回归模型是用于估计二项反应变量风险比的常用方法。先前的研究表明，这两种方法在产生相似的点估计值和标准误差方面表现相当。然而，它们在模型误设下的性能还不太清楚。
在这项模拟研究中，当对数链接函数误设或反应通过非线性关系（即截断反应）取决于预测因子时，比较了这两种模型的统计性能。
当链接函数误设或反应变量的概率分布在右尾截断时，对数二项式模型的点估计值存在偏差。截断观测值的百分比与偏差的存在呈正相关，如果其他被检查的参数固定，那么来自反应率较低的人群的观测值的偏差更大。相比之下，稳健泊松模型的点估计值无偏。
在模型误设下，稳健泊松模型通常更可取，因为它提供了风险比的无偏估计。
