How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates

Abstract

Although intention-to-treat analysis is a standard approach, additional supplemental analyses are often required to evaluate the biological relationship among interventions, intermediates, and outcomes. Therefore, we need to evaluate whether the effect of an intervention on a particular outcome is mediated by a hypothesized intermediate variable. To evaluate the size of the direct effect in the total effect, we applied the marginal structural model to estimate the average natural direct and indirect effects in a large-scale randomized controlled trial (RCT). Method The average natural direct effect is defined as the difference in the probability of a counterfactual outcome between the experimental and control arms, with the intermediate set to what it would have been, had the intervention been a control treatment. We considered two marginal structural models to estimate the average natural direct and indirect effects introduced by VanderWeele (Epidemiology 2009) and applied them in a large-scale RCT - the Candesartan Antihypertensive Survival Evaluation in Japan (CASE-J trial) - that compared angiotensin receptor blockers and calcium-channel blockers in high-risk hypertensive patients. There were no strong blood pressure-independent or dependent effects; however, a systolic blood pressure reduction of about 1.9 mmHg suppressed all events. Compared to the blood pressure-independent effects of calcium channel blockers, those of angiotensin receptor blockers contributed positively to cardiovascular and cardiac events, but negatively to cerebrovascular events. There is a particular condition for estimating the average natural direct effect. It is impossible to check whether this condition is satisfied with the available data. We estimated the average natural direct and indirect effects through the achieved systolic blood pressure in the CASE-J trial. This first application of estimating the average natural effects in an RCT can be useful for obtaining an in-depth understanding of the results and further development of similar interventions.