Estimating treatment effects conditional on observed covariates can improve
the ability to tailor treatments to particular individuals. Doing so
effectively requires dealing with potential confounding, and also enough data
to adequately estimate effect moderation. A recent influx of work has looked
into estimating treatment effect heterogeneity using data from multiple
randomized controlled trials and/or observational datasets. With many new
methods available for assessing treatment effect heterogeneity using multiple
studies, it is important to understand which methods are best used in which
setting, how the methods compare to one another, and what needs to be done to
continue progress in this field. This paper reviews these methods broken down
by data setting: aggregate-level data, federated learning, and individual
participant-level data. We define the conditional average treatment effect and
discuss differences between parametric and nonparametric estimators, and we
list key assumptions, both those that are required within a single study and
those that are necessary for data combination. After describing existing
approaches, we compare and contrast them and reveal open areas for future
research. This review demonstrates that there are many possible approaches for
estimating treatment effect heterogeneity through the combination of datasets,
but that there is substantial work to be done to compare these methods through
case studies and simulations, extend them to different settings, and refine
them to account for various challenges present in real data