Yamamuro et al. (2021) Multi-Mediator Simulation Dataset

A single dataset simulated from the data-generating process of the Yamamuro et al. (2021) multi-mediator simulation study: a time-varying binary treatment, a time-varying confounder, two sequential continuous mediators, and a discrete-time survival outcome over three visits. The large-sample true interventional effects for this process are documented below, so estimates obtained with mediation can be compared against known values.

Usage

yamamurodata

Format

A data frame with 29482 rows and 15 variables (long at-risk format: one row per subject per visit until the event):

id

Unique subject identifier.

time

Visit index (0, 1, 2).

V

Time-fixed binary baseline covariate.

A

Time-varying binary treatment.

L

Time-varying continuous confounder.

M1

First mediator (continuous), affected by A and L.

M2

Second mediator (continuous), affected by A, L, and M1.

Y

Event indicator (1 = event at this visit).

lag1_A, lag1_L, lag1_M1, lag1_M2

Previous-visit values (0 at baseline).

L0base, M10base, M20base

Baseline (visit 0) values of L, M1, M2, carried as time-fixed columns so the Monte Carlo simulation can be seeded via init_recode.

Source

Simulated from the data-generating process of Yamamuro, S., Shinozaki, T., Iimuro, S., & Matsuyama, Y. (2021). Mediational g-formula for time-varying treatment and repeated-measured multiple mediators. Statistical Methods in Medical Research, 30(8), 1782-1799. doi:10.1177/09622802211025988

Details

The within-visit ordering is A \(\to\) L \(\to\) M1 \(\to\) M2 \(\to\) Y; the full data-generating equations are translated from the SAS %simdata macro in the paper's supplementary material and are reproduced in data-raw/yamamurodata.R in the package source repository.

The published study design generates 1000 replicate datasets of \(n = 1000\) subjects and averages the estimates. A single replicate of that size is dominated by sampling error (between-replicate SD of the total effect is about 1.7 percentage points), so the dataset shipped here is one draw of \(n = 10000\) subjects (seed 2468) from the identical process, making single-dataset estimates informative.

Large-sample true values (risk differences in percentage points, computed from the data-generating process at \(n = 10^7\); Monte Carlo SE in parentheses):

Total effect (TE = \(E[Y_1] - E[Y_0]\))	\(-6.36\) (0.011)
Interventional direct effect (IDE)	\(-3.20\) (0.013)
Interventional indirect effect via M1	\(-2.29\) (0.011)
Interventional indirect effect via M2	\(-0.97\) (0.009)
Mediated-interaction residual (TE \(-\) overall)	\(0.10\) (0.016)

Every intervention in the interventional decomposition, including the references, draws each mediator jointly over time from an independently permuted marginal pool (Yamamuro et al. 2021, Eq. 2 and Fig. 3); the total effect is the natural-course contrast, so the residual row is non-zero.