  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.
