| Type: | Package |
| Title: | Mediation with External Summary Statistic Information |
| Version: | 0.1.2 |
| Description: | Fits the MESSI, hard constraint, and unconstrained models in Boss et al. (2023) <doi:10.48550/arXiv.2306.17347> for mediation analyses with external summary-level information on the total effect. |
| Maintainer: | Michael Kleinsasser <mkleinsa@umich.edu> |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Biarch: | true |
| Depends: | R (≥ 3.6.2) |
| Imports: | MASS, ggplot2, patchwork, progress |
| URL: | https://github.com/umich-cphds/messi |
| BugReports: | https://github.com/umich-cphds/messi/issues |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-01-06 15:03:34 UTC; mk |
| Author: | Jonathan Boss [aut], Bhramar Mukherjee [aut], Michael Kleinsasser [cre] |
| Repository: | CRAN |
| Date/Publication: | 2025-01-09 22:40:02 UTC |
Simulated data with real mediation effect
Description
Simulated data with real mediation effect
Usage
Med
Format
An object of class list of length 8.
Examples
data(Med)
Estimate hard constraint model parameters using cyclical coordinate descent.
Description
Estimate hard constraint model parameters using cyclical coordinate descent.
Usage
constrained.unpenalized(
Y,
M,
A,
C = NULL,
T.hat.external,
err.tol.out = 1e-08,
err.tol.med = 1e-08,
max.itr = 10000
)
Arguments
Y |
A (n x 1) continuous outcome vector. |
M |
A (n x p_m) matrix of mediators. |
A |
A (n x 1) vector of exposures. |
C |
A (n x p_c) matrix of confounders and adjustment covariates. If there are no confounders or adjustment covariates set C = NULL. |
T.hat.external |
External estimate of the total effect. |
err.tol.out |
Termination condition for cyclical coordinate descent algorithm with respect to the outcome model parameters. |
err.tol.med |
Termination condition for cyclical coordinate descent algorithm with respect to the mediator model parameters. |
max.itr |
Maximum number of iterations for cyclical coordinate descent algorithm. |
Value
A list containing point estimates of the hard constraint model parameters and an indicator of whether the algorithm converges.
Implementation of Mediation with External Summary Statistic Information (MESSI) from Boss et al. (2024).
Description
Implementation of Mediation with External Summary Statistic Information (MESSI) from Boss et al. (2024).
Usage
messi(
Y,
M,
A,
C = NULL,
method = "MESSI EB",
T.hat.external,
var.T.hat.external,
n.boot = 200,
s2.fixed = NULL
)
Arguments
Y |
A (n x 1) continuous outcome vector. |
M |
A (n x p_m) matrix of mediators. |
A |
A (n x 1) vector of exposures. |
C |
A (n x p_c) matrix of confounders and adjustment covariates. If there are no confounders or adjustment covariates set C = NULL. |
method |
A string specifying which method to use. Options include 'Unconstrained', 'Hard', 'MESSI EB', and 'MESSI Fixed'. Default is 'MESSI EB'. |
T.hat.external |
External estimate of the total effect. Set to NULL if method = 'Unconstrained'. |
var.T.hat.external |
Estimated variance of the external estimator of the total effect. Set to NULL if method = 'Unconstrained' or method = 'Hard'. |
n.boot |
Number of parametric bootstrap draws for obtaining quantile-based confidence intervals for the TE and NDE. Relevant for method = 'MESSI EB' and method = 'MESSI Fixed'. Can set to NULL for method = 'Unconstrained' and method = 'Hard'. |
s2.fixed |
Option to specify the tuning parameter s^2 in the MESSI model. Only use if method = 'MESSI Fixed'. |
Details
The MESSI EB method should be the default method if the user is not sure which method to use.
Value
A list containing the (1) point estimates and confidence intervals for the natural direct effect, the natural indirect effect, and the total effect (2) point estimates for all mediation model parameters (3) the asymptotic variance covariance matrix corresponding to alpha_a and beta_m.
Examples
data(Med)
Y = Med$Y
M = Med$M
A = Med$A
C = Med$C
T.hat.external = Med$T.hat.external
var.T.hat.external = Med$var.T.hat.external
test <- messi(Y = Y, M = M, A = A, C = C, method = 'Unconstrained', T.hat.external = T.hat.external,
var.T.hat.external = var.T.hat.external, s2.fixed = NULL)
n = Med$n
p = Med$p
plot_messi(n = n, alpha.a.hat = test$alpha.a.hat, beta.m.hat = test$beta.m.hat,
labels = paste0("M",1:p), asym.var.mat = test$asym.var.mat)
test <- messi(Y = Y, M = M, A = A, C = C, method = 'Hard', T.hat.external = T.hat.external,
var.T.hat.external = var.T.hat.external, s2.fixed = NULL)
Simulated data with null mediation effect
Description
Simulated data with null mediation effect
Usage
nullMed
Format
An object of class list of length 4.
Examples
data(nullMed)
Forestplot to Summarize Estimation and Inference on alpha_a and beta_m.
Description
Forestplot to Summarize Estimation and Inference on alpha_a and beta_m.
Usage
plot_messi(n, alpha.a.hat, beta.m.hat, labels, asym.var.mat)
Arguments
n |
Sample size of the analysis |
alpha.a.hat |
Estimate of alpha_a, a (p_m x 1) vector. |
beta.m.hat |
Estimate of beta_m, a (p_m x 1) vector. |
labels |
A (p_m x 1) vector of mediator names. Make sure that the labels are in the same order as the mediators appear in the design matrix. |
asym.var.mat |
Joint asymptotic variance-covariance matrix of alpha_a and beta_m, a (2p_m x 2p_m) matrix. |
Value
Data frames and forestplots summarizing alpha_a and beta_m estimation.
Examples
data(Med)
Y = Med$Y
M = Med$M
A = Med$A
C = Med$C
T.hat.external = Med$T.hat.external
var.T.hat.external = Med$var.T.hat.external
test <- messi(Y = Y, M = M, A = A, C = C, method = 'Unconstrained', T.hat.external = T.hat.external,
var.T.hat.external = var.T.hat.external, s2.fixed = NULL)
n = Med$n
p = Med$p
plot_messi(n = n, alpha.a.hat = test$alpha.a.hat, beta.m.hat = test$beta.m.hat,
labels = paste0("M",1:p), asym.var.mat = test$asym.var.mat)
Cyclical coordinate descent algorithm for the M-step in the EM Algorithm for the maximizing the soft constraint model likelihood.
Description
Cyclical coordinate descent algorithm for the M-step in the EM Algorithm for the maximizing the soft constraint model likelihood.
Usage
rand.eff.coord.desc.unpenalized(
Y,
M,
A,
C = NULL,
first.moment,
second.moment,
err.tol.out = 1e-08,
err.tol.med = 1e-08,
max.itr = 10000
)
Arguments
Y |
A (n x 1) continuous outcome vector. |
M |
A (n x p_m) matrix of mediators. |
A |
A (n x 1) vector of exposures. |
C |
A (n x p_c) matrix of confounders and adjustment covariates. If there are no confounders or adjustment covariates set C = NULL. |
first.moment |
Posterior expectation of the total effect parameter. |
second.moment |
Posterior expection of the squared total effect parameter. |
err.tol.out |
Termination condition for cyclical coordinate descent algorithm with respect to the outcome model parameters. |
err.tol.med |
Termination condition for cyclical coordinate descent algorithm with respect to the mediator model parameters. |
max.itr |
Maximum number of iterations for cyclical coordinate descent algorithm. |
Value
A list containing point estimates of the soft constraint model parameters and an indicator of whether the algorithm converges.
Estimate soft constraint model parameters using the EM algorithm.
Description
Estimate soft constraint model parameters using the EM algorithm.
Usage
rand.eff.unpenalized(
Y,
M,
A,
C = NULL,
rand.eff.mean,
rand.eff.var,
T.hat.external = T.hat.external,
var.T.hat.external = var.T.hat.external,
err.tol.out = 1e-08,
err.tol.med = 1e-08,
max.itr = 10000
)
Arguments
Y |
A (n x 1) continuous outcome vector. |
M |
A (n x p_m) matrix of mediators. |
A |
A (n x 1) vector of exposures. |
C |
A (n x p_c) matrix of confounders and adjustment covariates. If there are no confounders or adjustment covariates set C = NULL. |
rand.eff.mean |
Mean of the random effects distribution for the internal total effect parameter. |
rand.eff.var |
Variance of the random effects distribution for the internal total effect parameter. |
T.hat.external |
External estimate of the total effect. |
var.T.hat.external |
Estimated variance of the external total effect estimator. |
err.tol.out |
Termination condition for cyclical coordinate descent algorithm with respect to the outcome model parameters. |
err.tol.med |
Termination condition for cyclical coordinate descent algorithm with respect to the mediator model parameters. |
max.itr |
Maximum number of iterations for cyclical coordinate descent algorithm. |
Value
A list containing point estimates of the soft constraint model parameters and an indicator of whether the algorithm converges.
Estimate unconstrained model parameters.
Description
Estimate unconstrained model parameters.
Usage
unconstrained.unpenalized(Y, M, A, C = NULL)
Arguments
Y |
A (n x 1) continuous outcome vector. |
M |
A (n x p_m) matrix of mediators. |
A |
A (n x 1) vector of exposures. |
C |
A (n x p_c) matrix of confounders and adjustment covariates. If there are no confounders or adjustment covariates set C = NULL. |
Value
A list containing point estimates of the unconstrained model parameters.