How can one estimate the power for the detection of significant rG and rE in multivariate Cholesky models?
Specifically, the analysis employed a trivariate Cholesky (AE providing the best fit, with all significant rGs, and one significant rE). The CIs have been calculated for all estimates. The sample is on the small side: 200 same-sex pairs (half MZ, half DZ) and may have been underpowered for the other smaller rEs but I’m not sure what size effect I had enough power to detect.
I can estimate power for variance components in univariate decomposition models but cannot find anything specific to multivariate models or correlations. Any advice or script to get a power estimate specific to correlations would be super helpful!
Thanks in advance,
M
Printer-friendly version
Brad Verhulst is putting finishing touches on a paper on this topic. For the time being, with the Cholesky approach, it isn't very simple to test the power to detect rG or rE in a trivariate model. What you can do, however, is to see how much the fit deteriorates if you add a non-linear constraint to force the rG or rE to equal a particular value.
The difference in fit between the two models can be used as a non-centrality parameter for the chi-squared distribution.