Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions.

ABSTRACT: Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, ie, it is doubly robust, and can yield valid inference when used in conjunction with machine learning. It also has the advantage of easy-to-calculate analytic standard errors in contrast to the g-formula, which requires bootstrapping.

SUBMITTER: Schomaker M 

PROVIDER: S-EPMC6800798 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature