abYlm.Mglm: Adaptive Bootstrap Test in Mediation Analysis with Linear...

In abima: Adaptive Bootstrap Inference for Mediation Analysis with Enhanced Statistical Power

Adaptive Bootstrap Test in Mediation Analysis with Linear Models or Generalized Linear Models

Description

abYlm.Mglm conducts an adaptive bootstrap test in a mediation analysis in which mediator M is modeled by a generalized linear model (GLM) and outcome Y is modeled by a linear model (LM). The family of the generalized linear model is specified according to M.family.

Usage

abYlm.Mglm(
  S,
  M,
  Y,
  X = NULL,
  covariates_cfder = NULL,
  M.family = stats::gaussian(),
  s = 1,
  s_star = 0,
  B = 199,
  lambda = 2
)

Arguments

S	an n-by-1 vector for exposure.
M	an n-by-1 vector for mediator.
Y	an n-by-1 vector for outcome.
X	an n-by-p matrix for p confounders. Do not include intercept in matrix X. In the absence of confounders, matrix is not required to be specified.
covariates_cfder	a vector of p confounders to adjust mediation effects, and default is zero (or void). The length of this vector is p, the same as the column dimension of X.
M.family	the error distribution and link function for the mediator model. The default family is gaussian().
s	exposure level with default set at 1.
s_star	baseline exposure level with default set at 0.
B	the number of bootstrap samples with default set at 199.
lambda	the tuning parameter used in the adaptive bootstrap procedure with default set at 2.

Value

NIE refers to natural indirect effect, which is the estimated when a change of exposure S occurs from s_star to s.

p_value_NIE refers to the AB p value for NIE.

NDE refers to natural direct effect, which is estimated when exposure S changes from s_star to s.

p_value_NDE is the p value for NDE.

NTE is the estimated natural total effect when exposure S changes from s_star to s.

p_value_NTE is the p value for NTE.

References

He, Y., Song, P. X. K., and Xu, G. (2023), “Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis,” Journal of the Royal Statistical Society Series B: Statistical Methodology, qkad129. doi:10.1093/jrsssb/qkad129.

Examples

  ## Load libraries
  library(abima)

  set.seed(2)

  ## Set up parameters
  M.family <- poisson()

  simulation <- function(alpha_S = 0, beta_M = 0) {
    data <- generate_all_data(
      n = 500,
      alpha_S = alpha_S,
      beta_M = beta_M,
      M.family = M.family
    )
    S <- data$S
    M <- data$M
    Y <- data$Y
    X <- data$X

    out <- abYlm.Mglm(S,
                      M,
                      Y,
                      X,
                      M.family = M.family,
                      lambda = 2,
                      B = 199)
    out
  }


  simulation(1 / 8, 1 / 8)

  simulation(0, 0)

abima documentation built on Oct. 26, 2024, 1:08 a.m.