The reference-based approach to imputing missing data has become popular in clinical trials, as I’ve blogged about previously. In the standard approach, the multiple imputations are generated as draws from the posterior distribution under a Bayesian model. With a continuous outcome, each of the imputed datasets is analysed using a linear regression model for the outcome (typically measured at the final time point), with treatment group and some baseline variables as covariates.
In a new pre-print available on arXiv, in work by Marcel Wolbers and colleagues at Roche, we propose an alternative approach for reference-based imputation for continuous outcomes. This approach results in a treatment effect point estimate and (frequentist) standard error without any Monte-Carlo error.