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：在分析治疗与临床结局之间的关联时，常采用参数化多变量条件结局期望模型进行协变量校正。该结局模型中的治疗系数对应条件处理效应。模型标准化通常通过将模型预测值在目标协变量分布上求平均，生成边际处理效应的协变量校正估计值。传统模型标准化方法采用最大似然估计与非参数自助法。本研究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。我们将所提出的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，可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MIM与参数化结局模型联用时的有限样本性能。在二元结局、连续协变量、逻辑回归模型以及以边际对数比值比为目标效应度量的场景下，模拟结果验证了该方法的可行性。当参数建模假设成立时，MIM在目标协变量分布上可实现无偏估计、有效的覆盖率，并具有与传统模型标准化方法相当的精度与效率。