A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint.

ABSTRACT: 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 (ie, varying with patient characteristics). In this article, 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

SUBMITTER: Hoogland J 

PROVIDER: S-EPMC9291969 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature