Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re-invigorated?

Simulated treatment comparison (STC) is an established method for perform-ing population adjustment for the indirect comparison of two treatments,where individual patient data (IPD) are available for one trial but only aggre-gate level information is available for the other. The most commonly usedmethod is what we call‘standard STC’. Here we fit an outcome model usingdata from the trial with IPD, and then substitute mean covariate values fromthe trial where only aggregate level data are available, to predict what the firstof these trial's outcomes would have been if its population had been the sameas the second. However, this type of STC methodology does not involve simu-lation and can result in bias when the link function used in the outcome modelis non-linear. An alternative approach is to use the fitted outcome model tosimulate patient profiles in the trial for which IPD are available, but in theother trial's population. This stochastic alternative presents additional chal-lenges. We examine the history of STC and propose two new simulation-basedmethods that resolve many of the difficulties associated with the current sto-chastic approach. A virtue of the simulation-based STC methods is that themarginal estimands are then clearly targeted. We illustrate all methods using anumerical example and explore their use in a simulation study.