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之间关联时，通常采用条件结局期望的参数多变量模型来调整协变量。结局模型中的治疗系数针对的是条件性治疗效果。模型标准化通常用于在目标协变量分布上对模型预测进行平均化处理，从而生成边际治疗效果经协变量调整后的估计值。模型标准化的标准方法涉及最大似然估计和非参数自举法。我们提出了一种基于多重插补的新型通用模型标准化方法，该方法在结局模型为广义线性模型时易于应用。我们将所提出的方法称为多重插补边际化（MIM）。MIM包含两个主要阶段：合成数据集的生成及其分析。MIM兼容贝叶斯统计框架，该框架能自然实现不确定性的原则性传播，将分析整合到概率框架中，并允许纳入先验证据。我们通过模拟研究评估了MIM在参数化结局模型下的有限样本性能。模拟结果在二元结局、连续型协变量、逻辑结局模型以及以边际对数比值比作为目标效应量度的场景中验证了其原理可行性。当参数化建模假设成立时，MIM在目标协变量分布上能实现无偏估计、有效的覆盖率，且精度和效率与标准模型标准化方法相当。