Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis

Abstract

Background: Surgical interventions allow for tailoring of treatment to individual patients and implementation may vary with surgeon and healthcare provider. In addition, in clinical trials assessing two competing surgical interventions, the treatments may be accompanied by co-interventions. Aims: This study explores the use of causal mediation analysis to (1) delineate the treatment effect that results directly from the surgical intervention under study and the indirect effect acting through a co-intervention and (2) to evaluate the benefit of the surgical intervention if either everybody in the trial population received the co-intervention or nobody received it. Methods: Within a counterfactual framework, relevant direct and indirect effects of a surgical intervention are estimated and adjusted for confounding via parametric regression models, for the situation where both mediator and outcome are binary, with baseline stratification factors included as fixed effects and surgeons as random intercepts. The causal difference in probability of a successful outcome (estimand of interest) is calculated using Monte Carlo simulation with bootstrapping for confidence intervals. Packages for estimation within standard statistical software are reviewed briefly. A step by step application of methods is illustrated using the Amaze randomised trial of ablation as an adjunct to cardiac surgery in patients with irregular heart rhythm, with a co-intervention (removal of the left atrial appendage) administered to a subset of participants at the surgeon’s discretion. The primary outcome was return to normal heart rhythm at one year post surgery. Results: In Amaze, 17% (95% confidence interval: 6%, 28%) more patients in the active arm had a successful outcome, but there was a large difference between active and control arms in the proportion of patients who received the co-intervention (55% and 30%, respectively). Causal mediation analysis suggested that around 1% of the treatment effect was attributable to the co-intervention (16% natural direct effect). The controlled direct effect ranged from 18% (6%, 30%) if the co-intervention were mandated, to 14% (2%, 25%) if it were prohibited. Including age as a moderator of the mediation effects showed that the natural direct effect of ablation appeared to decrease with age. Conclusions: Causal mediation analysis is a useful quantitative tool to explore mediating effects of co-interventions in surgical trials. In Amaze, investigators could be reassured that the effect of the active treatment, not explainable by differential use of the co-intervention, was significant across analyses.

A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies

“…In most cases, the natural direct and indirect effects are recommended when the aim is to explain the causal relationship between an exposure and an outcome through 1 or more mediators (eg, the natural indirect effect of intensive blood pressure therapy on cardiovascular events mediated through low diastolic blood pressure had a hazard ratio of 1.12 [95% CI, 1.06-1.18] and the natural direct effect not mediated through low diastolic blood pressure had a hazard ratio of 0.63 [95% CI, 0.50-0.78]) . If the study objective is to estimate the causal relationship between an exposure and an outcome while a mediator is fixed at a constant level uniformly across the population, the controlled direct effect is recommended (eg, the causal relationship between ablation surgery and returning to sinus rhythm if no patient in the target population had the left atrial appendage removed had a hazard ratio of 0.14 [95% CI, 0.02-0.25] on the probability difference scale) …”

“…19 If the study objective is to estimate the causal relationship between an exposure and an outcome while a mediator is fixed at a constant level uniformly across the population, the controlled direct effect is recommended (eg, the causal relationship between ablation surgery and returning to sinus rhythm if no patient in the target population had the left atrial appendage removed had a hazard ratio of 0.14 [95% CI, 0.02-0.25] on the probability difference scale). 35 The estimation of exposure-mediator and mediator-outcome relationships will often require weaker assumptions than the estimation of direct and indirect effects. For this reason, as well as to provide more insight into the possible mechanisms of interest, authors should always report relevant estimates for the exposuremediator and mediator-outcome relationships.…”

Understanding how health interventions or exposures produce their effects using mediation analysis

“…Finally, in some situations, mediation analysis can be used in trials to estimate the direct effect (box 1) of an intervention on an outcome. In this setting, researchers might want to isolate an intervention effect that is not mediated through a variable on the causal pathway (eg, an unintended co-intervention during planned routine cardiac surgery1718).…”

“…The difference in the probability of le atrial appendage removal between groups led investigators to conduct a secondary mediation analysis to investigate (a) the direct effect of ablation on outcome and the indirect effect acting through the co-intervention (le atrial appendage removal), and (b) the controlled direct effect of ablation if everybody in the trial population received the co-intervention. 17 The directed acyclic graph (below) depicts the proposed causal relation between the exposure (ablation), mediator (le atrial appendage removal), and outcome (return to sinus rhythm). The directed acyclic graph also depicts a confounder variable which contains a list of potential confounding variables (eg, heart rhythm at baseline, age) which could distort the mediator-outcome associations and would need to be controlled for.…”