When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the model predictions over the target covariate distribution, and generate a covariate-adjusted estimate of the marginal treatment effect. The standard approach to model-based standardization involves maximum-likelihood estimation and use of the non-parametric bootstrap. We introduce a novel, general-purpose, model-based standardization method based on multiple imputation that is easily applicable when the outcome model is a generalized linear model. We term our proposed approach multiple imputation marginalization (MIM). MIM consists of two main stages: the generation of synthetic datasets and their analysis. MIM accommodates a Bayesian statistical framework, which naturally allows for the principled propagation of uncertainty, integrates the analysis into a probabilistic framework, and allows for the incorporation of prior evidence. We conduct a simulation study to benchmark the finite-sample performance of MIM in conjunction with a parametric outcome model. The simulations provide proof-of-principle in scenarios with binary outcomes, continuous-valued covariates, a logistic outcome model and the marginal log odds ratio as the target effect measure. When parametric modeling assumptions hold, MIM yields unbiased estimation in the target covariate distribution, valid coverage rates, and similar precision and efficiency than the standard approach to model-based standardization.

翻译：在研究治疗与临床结局的关联性时，常采用参数化条件期望多变量模型校正协变量。该模型的治疗系数用于估计条件治疗效应。模型标准化通常通过将模型预测值平均至目标协变量分布，生成协变量校正后的边际治疗效应估计值。标准模型标准化方法采用极大似然估计与非参数自助法。我们提出一种基于多重插补的新型通用模型标准化方法，该方法适用于结局模型为广义线性模型的情形，并将该方法命名为多重插补边际化(MIM)。MIM包含两个主要阶段：合成数据集的生成及其分析。该方法兼容贝叶斯统计框架，能够自然实现不确定性的原则性传播，将分析整合至概率框架中，并允许纳入先验证据。我们通过模拟研究评估MIM与参数化结局模型联用的有限样本表现。模拟实验证明，在二分类结局、连续型协变量、逻辑斯蒂回归模型及以边际对数比值比为效应度量指标的场景中，该原理具有可行性。当参数化建模假设成立时，MIM在目标协变量分布中可获得无偏估计、有效覆盖率，并在精度与效率方面与传统模型标准化方法相当。