A Causal Framework for Making Individualized Treatment Decisions in Oncology.

ABSTRACT: We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT's entry criteria, or a treatment's effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment-covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new pat

SUBMITTER: Msaouel P 

PROVIDER: S-EPMC9406391 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature