TY - JOUR
T1 - A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention
AU - Coffman, Donna L.
AU - Dziak, John
AU - Litson, Kaylee
AU - Chakraborti, Yajnaseni
AU - Piper, Megan E.
AU - Li, Runze
N1 - Funding Information:
Funding: This work was supported by grants 1R01 CA229542-01 from the National Cancer Institute and the National Institutes of Health Office of Behavioral and Social Science Research, 5R01HL109031 from the National Heart, Lung, and Blood Institute, and P50 DA039838 from the National Institute on Drug Abuse.
Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - 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 at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.
AB - 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 at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.
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U2 - 10.1080/00273171.2022.2149449
DO - 10.1080/00273171.2022.2149449
M3 - Article
C2 - 36622859
AN - SCOPUS:85146703197
SN - 0027-3171
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
ER -