Causal mediation analysis estimating interventional effects mapped to a target trial
The R packagemedRCT for causal mediation analysis supports the estimation of interventional effects (VanderWeele, Vansteelandt, and Robins 2014), specifically interventional effects that are defined such that they map explicitly to a “target trial” (Hernán and Robins 2016), as recently proposed by Moreno-Betancur et al. (2021). In the target trial, the treatment strategies are specified to reflect hypothetical interventions targeting and thus shifting the joint distribution of the mediators.medRCT can accommodate any number of potentially correlated mediators, including mediators that are not of primary interest but that are intermediate (exposure-induced) mediator-outcome confounders.
Installation
ThemedRCT package is not yet available on CRAN. You can install the latest stable version fromGitHub using the following command:
remotes::install_github("T0ngChen/medRCT")Example
Using a simulated dataset based on a published case study from the Longitudinal Study of Australian Children (Goldfeld et al. 2023), we illustrate how to usemedRCT to estimate the interventional effects that emulate a target trial. Specifically, we aim to estimate the difference in expected outcome (risk of child mental health problems) under exposure (low family socioeconomic position) with versus without a hypothetical intervention that individually shifts the distribution of each mediator (parental mental health and preschool attendance) to the levels in the unexposed (high family socioeconomic position), while accounting for baseline confounders, an intermediate (exposure-induced) mediator-outcome confounder (family stressful life events), and correlations amongst mediators.
We begin by loading the library and dataset, and defining the confounder vector.
# Load the medRCT packagelibrary(medRCT)# Set a seed for reproducibilityset.seed(2025)# Display the first few rows of the datasethead(LSACdata)#> child_sex child_atsi mat_cob mat_engl mat_age sep fam_stress parent_mh preschool_att child_mh#> 1 0 1 0 0 1 0 0 0 1 0#> 2 NA 0 0 0 NA 0 NA NA 0 0#> 3 NA 0 0 0 NA 0 NA NA 0 1#> 4 NA 0 0 0 NA 0 NA NA 0 0#> 5 1 0 0 0 1 1 0 0 0 1#> 6 1 0 0 0 1 0 1 1 0 1#> child_SDQscore#> 1 8.924660#> 2 7.349826#> 3 12.824643#> 4 6.611369#> 5 10.329341#> 6 13.552515# Define confounders for the analysisconfounders<-c("child_sex","child_atsi","mat_cob","mat_engl","mat_age")Next we run the analyses, estimating interventional effects for a hypothetical intervention that shifts the distribution of each mediator individually.Note 1: the dataset has missing data. Incomplete records are by default deleted before the analysis.Note 2: It is recommended to perform the analysis with at least 200 Monte Carlo simulations by settingmcsim = 200. For illustration purposes, we usemcsim = 50, which takes approximately 90 seconds to run.
# Estimate interventional effects for a hypothetical intervention# that shifts the distribution of each mediator individuallymed_res<-medRCT( dat=LSACdata, exposure="sep", outcome="child_mh", mediators=c("parent_mh","preschool_att"), intermediate_confs="fam_stress",# intermediate confounders confounders=confounders, bootstrap=TRUE, intervention_type="shift_k", mcsim=50)#> Conducting complete case analysis, 2499 observations were excluded due to missing data.#> Note: It is recommended to run analysis with no fewer than 200 Monte Carlo simulations.# Summarise the resultssummary(med_res)#>#> Estimated interventional indirect effect:#>#> Estimate Std. Error CI Lower CI Upper p-value#> IIE_1 (p_trt - p_1) 0.011155 0.004181 0.002814 0.019203 0.0076#> IIE_2 (p_trt - p_2) -0.000763 0.002501 -0.005443 0.004362 0.7604#> TCE (p_trt - p_ctr) 0.128669 0.024554 0.082420 0.178668 1.6e-07#>#> Estimated interventional direct effect:#>#> Estimate Std. Error CI Lower CI Upper p-value#> IDE_1 (p_1 - p_ctr) 0.1175 0.0247 0.0712 0.1679 1.9e-06#> IDE_2 (p_2 - p_ctr) 0.1294 0.0244 0.0833 0.1789 1.1e-07#>#> Estimated expected outcome in each trial arm:#>#> Estimate Std. Error CI Lower CI Upper p-value#> p_1 0.3302 0.0225 0.2872 0.3755 <2e-16#> p_2 0.3421 0.0221 0.2995 0.3862 <2e-16#> p_ctr 0.2127 0.0100 0.1922 0.2315 <2e-16#> p_trt 0.3413 0.0223 0.2987 0.3860 <2e-16#>#> Sample Size: 2608#>#> Simulations: 50Based on the estimated interventional effect (IIE_1), a hypothetical intervention improving the mental health of parents of children from families with low socioeconomic position to the levels of those from families with high socioeconomic position could potentially prevent 1 per 100 cases of child mental health problems. Meanwhile, the effect of a hypothetical intervention on preschool attendance (IIE_2) is negligible.
For detailed guidance on using the package to handle more complex scenarios, please refer to thevignette.
Citation
For work involving themedRCT R package, please cite the following:
@software{Chen2024medRCT, author= {Tong Chen and Margarita Moreno-Betancur and S. Ghazaleh Dashti}, title= {medRCT: Causal mediation analysis estimating interventional effects mapped to a target trial}, year= {2025}, url= {https://t0ngchen.github.io/medRCT/}, note= {R package version0.1.0} }@article{Moreno2021Mediation, author={Margarita Moreno-Betancur and Paul Moran and Denise Becker and George C Patton and John B Carlin}, title={Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators}, journal={Statistical Methodsin Medical Research}, volume={30}, number={6}, pages={1395--1412}, year={2021}, URL={https://doi.org/10.1177/0962280221998409}, doi={10.1177/0962280221998409}, publisher={SAGE Publications Ltd} }
