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Published on: October 20, 2012
Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis
Linda Sharples1, Olympia Papachristofi1,2, Saleema Rex1,3
1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
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.
Insights
Causal mediation analysis quantifies surgical intervention effects, distinguishing direct impacts from co-intervention influences. This method confirmed significant benefits of ablation surgery, independent of co-interventions, in a cardiac rhythm trial.
Area of Science:
- Biostatistics
- Surgical Research
- Clinical Trials
Background:
- Surgical interventions are individualized, leading to variations in practice and co-interventions within clinical trials.
- Co-interventions, often surgeon-dependent, can complicate the assessment of a primary surgical treatment's true effect.
Purpose of the Study:
- To apply causal mediation analysis to distinguish direct surgical effects from indirect effects mediated by co-interventions.
- To evaluate the surgical intervention's benefit under scenarios where the co-intervention is universally applied or completely absent.
Main Methods:
- Employed a counterfactual framework to estimate direct and indirect effects, adjusting for confounding using parametric regression.
- Utilized binary mediator and outcome models with baseline stratification and random surgeon intercepts.
- Applied Monte Carlo simulation with bootstrapping for confidence intervals, illustrated with the Amaze trial data.
Main Results:
- Causal mediation analysis in the Amaze trial indicated that approximately 1% of the treatment effect was due to the co-intervention (left atrial appendage removal).
- The natural direct effect of ablation was estimated at 16%, with controlled direct effects ranging from 14% to 18% depending on co-intervention mandate.
- Age was identified as a moderator, with the direct effect of ablation potentially decreasing with age.
Conclusions:
- Causal mediation analysis provides a robust method for dissecting co-intervention effects in surgical trials.
- The Amaze trial results demonstrate a significant direct treatment effect of ablation, independent of the co-intervention's variable use.
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