simABC.model: Simulate data and estimate lmm within stepped wedge design...

In trutscheld/SWDsampling: Sampling data within stepped wedge designs, power calculation and simulations

Description

For a given one scenario build mean intensities of SWD model, sample data and estimate treatment parameter using a linear mixed model TP number of timepoints, I number of cluster. The design matrix has to be coded by zeros and ones.

Usage

simABC.model(I, TP, mu, theta, beta.j, sigma.alpha, X.i.j.0, N, sigma.e,
  sigma.ind = NULL, A = NULL, B = "0", C = 0, D = 0)

Arguments

I	number of clusters (design parameter)
TP	number of timepoints (design parameter)
mu	baseline mean (model parameter)
theta	treatment effect (model parameter)
beta.j	vector of time trents (model parameter)
sigma.alpha	between cluster variability as standard deviation (model parameter)
X.i.j.0	assumed treatment model matrix for a SWD study (model parameter)
N	number of individuals (fixed) for all clusters and timepoints
sigma.e	random error variability as standard deviation (model parameter)
sigma.ind	individual variability as standard deviation (model parameter), if it is an longitudinal model, by default (NULL) it is an cross-sectional model
A	derivation from perfect 100 percent effectiveness pattern (simulation parameter)
B	timepoint of cluster loss (simulation parameter) with 4 possibilities: "0": default - no cluster at no timepoint get lost, "1" - Cluster missing at random from timepoint 2 untill TP, "2" - Cluster is missing at beginning (1/3 of timepoints after the first), "3" - Cluster is missing at end (1/3 of the last timepoints).
C	number of cluster loss (simulation parameter), by default zero. If a cluster get lost from time point i, all indiviual responses of that cluster will be deleted from timepoint i until timpeoint TP (end).
D	number of individuals loss (simulation parameter), by default zero. If not zero, then individual responses to delete are selected at random from timepoints and clusters.

Value

linear mixed model #@examples

trutscheld/SWDsampling documentation built on June 1, 2019, 1:52 a.m.