simulate_funmediation_example: simulate_funmediation_example function
In funmediation: Functional Mediation for a Distal Outcome

Description Usage Arguments Value Examples

View source: R/simulate_funmediation_example.R

Simulates a dataset for demonstrating the funmediation function.

simulate_funmediation_example(
  nsub = 500,
  nlevels = 2,
  ntimes = 100,
  observe_rate = 0.4,
  alpha_int = function(t) {     return(t^0.5) },
  alpha_X = function(t) {     return(-(t/2)^0.5) },
  beta_M = function(t) {     (1/2) * (exp(t) - 1) },
  beta_int = 0,
  beta_X = 0.2,
  sigma_Y = 1,
  sigma_M_error = 2,
  rho_M_error = 0.8,
  simulate_binary_Y = FALSE,
  make_covariate_S = FALSE
)

`nsub`	Number of subjects
`nlevels`	Number of treatment groups or levels on the treatment variable X. Subjects are assumed to be randomly assigned to each level with equal probability (i.e., the probability per level is 1/nlevel). Default is 2 for a randomized controlled trial with a control group X=0 and an experimental group X=1. There should not be less than 2 or more than 5 groups for purposes of this function.
`ntimes`	Number of potential times that could be observed on each subject
`observe_rate`	Proportion of potential times on which there are actually observations. Not all times are observed; this is assumed to be completely random and to be done by design to reduce participant burden.
`alpha_int`	Function representing the time-varying mean of mediator variable for the level of treatment with all treatment dummy codes X set to 0 (e.g., the control group).
`alpha_X`	Function representing the time-varying effect of X on the mediator (if there are two treatment levels) or a list of nlevels-1 functions representing the effect of receiving each nonzero level of X rather than control (if there are more than two treatment levels).
`beta_M`	Function representing the functional coefficient for cumulative (scalar-on-function) effect of the mediator M on the treatment Y adjusting for the treatment X
`beta_int`	Mean of Y if the X is zero and M is the 0 function
`beta_X`	Numeric value representing the direct effect of X on Y after adjusting for M (if there are two treatment levels) or a vector of nlevels-1 numeric values (if there are more than two treatment levels)
`sigma_Y`	Error standard deviation of the outcome Y (conditional on treatment and mediator trajectory)
`sigma_M_error`	Error standard deviation of the mediator M (conditional on treatment and time)
`rho_M_error`	Autoregressive correlation coefficient of the error in the mediator M, from one observation to the next
`simulate_binary_Y`	Whether Y should be generated from a binary logistic (TRUE) or Gaussian (FALSE) model
`make_covariate_S`	Whether to generate a random binary covariate S at the subject (i.e., time-invariant) level. It will be generated to have zero population-level relationship to the outcome.

A list with the following components:

time_grid: The time grid for interpreting functional coefficients.
true_alpha_int: True value of the time-varying alpha_int parameter, representing the time-specific mean of the mediator M when the treatment value X is 0.
true_alpha_X: True value of the time-varying alpha_X parameter, representing the effect of X on M. This is a single number if nlevels=2, or a vector of effects if nlevels>2.
true_beta_int: True value of the beta_M parameter, representing the mean of the outcome Y when X=0 and M=0.
true_beta_M: True value of the beta_M parameter, representing the functional effect of treatment on the outcome Y.
true_beta_X: True value of the beta_X parameter, representing the effect of treatment on the outcome Y adjusting for the mediator. This is a single function if nlevels=2, or a vector of functions if nlevels>2.
true_indirect: True value of the indirect parameter, representing the indirect (mediated) effect of treatment on the outcome Y. This is a single number if nlevels=2, or a vector of effects if nlevels>2.
dataset: The simulated longitudinal dataset in long form.

set.seed(123)
# Simplest way to call the function:
simulation_all_defaults <- simulate_funmediation_example()
summary(simulation_all_defaults)
head(simulation_all_defaults)
# Changing the sample size to be larger:
simulation_larger <- simulate_funmediation_example(nsub=10000)
summary(simulation_larger)
# Changing the effect of the mediator to be null:
simulation_null <- simulate_funmediation_example(beta_M=function(t) {return(0*t)})
summary(simulation_null)
# Simulating a exposure variable with three levels (two dichotomous dummy codes)
simulation_three_group <- simulate_funmediation_example(nlevels=3,
                              alpha_X = list(function(t) {return(.1*t)},
                                             function(t) {return(-(t/2)^.5)}),
                              beta_X = c(-.2,.2))
print(summary(simulation_three_group));