Causal inference with marginal structural modeling for longitudinal data in laparoscopic surgery: A technical note

Abstract

Causal inference prevails in the field of laparoscopic surgery. Once the causality between an intervention and outcome is established, the intervention can be applied to a target population to improve clinical outcomes. In many clinical scenarios, interventions are applied longitudinally in response to patients’ conditions. Such longitudinal data comprise static variables, such as age, gender, and comorbidities; and dynamic variables, such as the treatment regime, laboratory variables, and vital signs. Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome; in such cases, simple adjustment with a conventional regression model will bias the effect sizes. To address this, numerous statistical methods are being developed for causal inference; these include, but are not limited to, the structural marginal Cox regression model, dynamic treatment regime, and Cox regression model with time-varying covariates. This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery.

Keywords

• Causal inference
• Laparoscopic surgery
• Machine learning
• Marginal structural modeling