  Randomized trials typically estimate average relative treatment effects, but
decisions on the benefit of a treatment are possibly better informed by more
individualized predictions of the absolute treatment effect. In case of a
binary outcome, these predictions of absolute individualized treatment effect
require knowledge of the individual's risk without treatment and incorporation
of a possibly differential treatment effect (i.e. varying with patient
characteristics). In this paper we lay out the causal structure of
individualized treatment effect in terms of potential outcomes and describe the
required assumptions that underlie a causal interpretation of its prediction.
Subsequently, we describe regression models and model estimation techniques
that can be used to move from average to more individualized treatment effect
predictions. We focus mainly on logistic regression-based methods that are both
well-known and naturally provide the required probabilistic estimates. We
incorporate key components from both causal inference and prediction research
to arrive at individualized treatment effect predictions. While the separate
components are well known, their successful amalgamation is very much an
ongoing field of research. We cut the problem down to its essentials in the
setting of a randomized trial, discuss the importance of a clear definition of
the estimand of interest, provide insight into the required assumptions, and
give guidance with respect to modeling and estimation options. Simulated data
illustrates the potential of different modeling options across scenarios that
vary both average treatment effect and treatment effect heterogeneity. Two
applied examples illustrate individualized treatment effect prediction in
randomized trial data.
