A causal approach to functional mediation analysis with application to a smoking cessation intervention

TL;DR

This paper introduces a causal framework for analyzing mediation effects in longitudinal data, specifically applied to smoking cessation, using a new estimation method and an R package for practical implementation.

Contribution

It develops a novel causal mediation analysis method for longitudinal mediators and provides an R package, funmediation, for applying this technique in real-world studies.

Findings

Bootstrap test for indirect effect demonstrated via simulation

Empirical example estimated smoking abstinence from craving patterns

Method effectively captures causal mediation in longitudinal data

Abstract

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.