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Total, Direct, and Indirect Effects in Continuous-Time Mediation Model

Ivan Jacob Agaloos Pesigan

2025-01-19

Source: vignettes/med.Rmd
med.Rmd

The cTMed package offers tools for estimating and quantifying uncertainty in total, direct, and indirect effects within continuous-time mediation models across various time intervals using the delta and Monte Carlo methods. To implement these approaches, estimates from a continuous-time vector autoregressive (CT-VAR) model are required, particularly the drift matrix and its corresponding sampling variance-covariance matrix. For guidance on fitting CT-VAR models using the dynr or OpenMx packages, refer to Fit the Continuous-Time Vector Autoregressive Model Using the dynr Package and Fit the Continuous-Time Vector Autoregressive Model Using the OpenMx Package, respectively.

# Drift matrix
phi <- matrix(
  data = c(
    -0.357, 0.771, -0.450,
    0.0, -0.511, 0.729,
    0, 0, -0.693
  ),
  nrow = 3
)
colnames(phi) <- rownames(phi) <- c("x", "m", "y")
# Sampling variance-covariance matrix
vcov_phi_vec <- matrix(
  data = c(
    0.00843, 0.00040, -0.00151, -0.00600, -0.00033, 0.00110,
    0.00324, 0.00020, -0.00061, 0.00040, 0.00374, 0.00016,
    -0.00022, -0.00273, -0.00016, 0.00009, 0.00150, 0.00012,
    -0.00151, 0.00016, 0.00389, 0.00103, -0.00007, -0.00283,
    -0.00050, 0.00000, 0.00156, -0.00600, -0.00022, 0.00103,
    0.00644, 0.00031, -0.00119, -0.00374, -0.00021, 0.00070,
    -0.00033, -0.00273, -0.00007, 0.00031, 0.00287, 0.00013,
    -0.00014, -0.00170, -0.00012, 0.00110, -0.00016, -0.00283,
    -0.00119, 0.00013, 0.00297, 0.00063, -0.00004, -0.00177,
    0.00324, 0.00009, -0.00050, -0.00374, -0.00014, 0.00063,
    0.00495, 0.00024, -0.00093, 0.00020, 0.00150, 0.00000,
    -0.00021, -0.00170, -0.00004, 0.00024, 0.00214, 0.00012,
    -0.00061, 0.00012, 0.00156, 0.00070, -0.00012, -0.00177,
    -0.00093, 0.00012, 0.00223
  ),
  nrow = 9
)

In this example, we aim to calculate the total, direct, and indirect effects of x on y, mediated through m, over time intervals ranging from 0 to 10.

# time intervals
delta_t <- seq(from = 0, to = 10, length.out = 1000)

Delta Method 

library(cTMed)
delta <- DeltaMed(
  phi = phi,
  vcov_phi_vec = vcov_phi_vec,
  delta_t = delta_t,
  from = "x",
  to = "y",
  med = "m",
  ncores = parallel::detectCores() # use multiple cores
)
plot(delta)

Monte Carlo Method 

mc <- MCMed(
  phi = phi,
  vcov_phi_vec = vcov_phi_vec,
  delta_t = delta_t,
  from = "x",
  to = "y",
  med = "m",
  R = 20000L,
  ncores = parallel::detectCores() # use multiple cores
)
plot(mc)

References

Deboeck, P. R., & Preacher, K. J. (2015). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 61–75. https://doi.org/10.1080/10705511.2014.973960
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Ryan, O., & Hamaker, E. L. (2021). Time to intervene: A continuous-time approach to network analysis and centrality. Psychometrika, 87(1), 214–252. https://doi.org/10.1007/s11336-021-09767-0