Two-stage g-computation: Evaluating Treatment and Intervention Impacts in Observational Cohorts When Exposure Information Is Partly Missing.

ABSTRACT: Illustrations of the g-computation algorithm to evaluate population average treatment and intervention effects have been predominantly implemented in settings with complete exposure information. Thus, worked examples of approaches to handle missing data in this causal framework are needed to facilitate wider use of these estimators. We illustrate two-stage g-computation estimators that leverage partially observed information on the full study sample and complete exposure information on a subset to estimate causal effects. In a hypothetical cohort of 1,623 human immunodeficiency virus (HIV)-positive women with 30% complete opioid prescription information, we illustrate a two-stage extrapolation g-computation estimator for the average treatment effect of shorter or longer duration opioid pre

SUBMITTER: Breger TL 

PROVIDER: S-EPMC8725064 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature