  The recently published ICH E9 addendum on estimands in clinical trials
provides a framework for precisely defining the treatment effect that is to be
estimated, but says little about estimation methods. Here we report analyses of
a clinical trial in type 2 diabetes, targeting the effects of randomised
treatment, handling rescue treatment and discontinuation of randomised
treatment using the so-called hypothetical strategy. We show how this can be
estimated using mixed models for repeated measures, multiple imputation,
inverse probability of treatment weighting, G-formula and G-estimation. We
describe their assumptions and practical details of their implementation using
packages in R. We report the results of these analyses, broadly finding similar
estimates and standard errors across the estimators. We discuss various
considerations relevant when choosing an estimation approach, including
computational time, how to handle missing data, whether to include post
intercurrent event data in the analysis, whether and how to adjust for
additional time-varying confounders, and whether and how to model different
types of ICE separately.
