ICES, Toronto, ON, Canada.
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Stat Methods Med Res. 2024 Jun;33(6):1055-1068. doi: 10.1177/09622802241247742. Epub 2024 Apr 24.
We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a bootstrap variance estimator for estimating the standard error of the risk difference and the log-relative risk. The performance of these methods was compared with that of direct weighting. Both the direct weighting approach and MSMs based on weighted univariate GLMs resulted in the identical estimates of risk differences and relative risks. When sample sizes were small to moderate, the use of an MSM with a bootstrap variance estimator tended to result in the most accurate estimates of standard errors. When sample sizes were large, the direct weighting approach and an MSM with a bootstrap variance estimator tended to produce estimates of standard error with similar accuracy. When using a MSM to estimate risk differences and relative risks, in general it is preferable to use a bootstrap variance estimator than the robust variance estimator. We illustrate the application of the different methods for estimating risks differences and relative risks using an observational study on the effect on mortality of discharge prescribing of a beta-blocker in patients hospitalized with acute myocardial infarction.
我们使用蒙特卡罗模拟比较了基于加权单变量广义线性模型 (GLM) 的边缘结构模型 (MSM) 在观察性研究中估计二分类结局风险差异和相对风险的性能。我们考虑了基于倾向评分的四种不同权重：以平均处理效应为目标估计量的逆概率治疗权重、治疗中估计平均处理效应的权重、匹配权重和重叠权重。我们考虑了从 500 到 10000 的样本量，并允许治疗的流行率从 0.1 到 0.9。我们检查了使用具有独立工作相关矩阵的广义估计方程的稳健方差估计器和用于估计风险差异和对数相对风险标准误的自举方差估计器。这些方法的性能与直接加权的方法进行了比较。直接加权方法和基于加权单变量 GLM 的 MSM 都导致了风险差异和相对风险的相同估计值。当样本量较小时，使用具有自举方差估计器的 MSM 往往会导致最准确的标准误估计。当样本量较大时，直接加权方法和具有自举方差估计器的 MSM 往往会产生具有相似准确性的标准误估计。当使用 MSM 估计风险差异和相对风险时，通常最好使用自举方差估计器而不是稳健方差估计器。我们使用急性心肌梗死后β受体阻滞剂出院处方对死亡率影响的观察性研究来说明不同方法估计风险差异和相对风险的应用。
