Graphing and reporting heterogeneous treatment effects through reference classes.

ABSTRACT:

Background

Exploration and modelling of heterogeneous treatment effects as a function of baseline covariates is an important aspect of precision medicine in randomised controlled trials (RCTs). Randomisation generally guarantees the internal validity of an RCT, but heterogeneity in treatment effect can reduce external validity. Estimation of heterogeneous treatment effects is usually done via a predictive model for individual outcomes, where one searches for interactions between treatment allocation and important patient baseline covariates. However, such models are prone to overfitting and multiple testing and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model.

Methods

SUBMITTER: Watson JA 

PROVIDER: S-EPMC7204233 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature