This functions helps to evaluate the consequences of ignoring a random slope at the cluster level.
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The design effect (DEFT) is the ratio of the standard error from the correct three-level model to the standard error from the misspecified model omitting the cluster-level random slope. The standard error for the misspecified model is calculated by assuming that the cluster-level random slope variance is added to the subject-level random slope.
The approximate Type I error under the miss-specified model is also calculated.
The effect of wrongly ignoring a third-level random slope on the Type I errors, depends on
n1, n2, n3, icc_slope, and,
data.frame with the columns
n1, n2, n3, icc_slope,
var_ratio, DEFT, and,
approx_type1. The number of rows of the
data.frame will be equals to the number of
different combination of parameters values specified with study_parameters.
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