Sample Size Estimation for Linear Mixed Models with Dependent End Points
The primary objective is sample size estimation in linear mixed model settings. Sample size estimation is an important component of planning a well thought out scientific experiment. Whenever sample size estimation is performed, taking into account a priori model based inferences will provide a sample size estimate that will achieve the desired power without inflating the type I error rate of the study. One common practice is a traditional approach cited in the literature that uses the largest sample size after you Bonferroni the type I error rate to estimate sample sizes as such. We are going to take into account multiplicity using the tree spanning (graph-based) algorithm and improve on just a Bonferroni correction so that we can estimate somewhat lower sample sizes for linear mixed model settings. We present a tree spanning algorithm that is a fast simple novel approach for estimating sample size which focuses on controlling the family-wise error in LMM's with arbitrary dependency structures. This method warrants more powerful bounds compared to the Bonferroni which tends to be more conservative for large set of comparisons. This proposed methodology will yield smaller estimators for sample size may be obtained to make better use of time and resources in experimental settings.
Nsiah-Nimo, Michael, "Sample Size Estimation for Linear Mixed Models with Dependent End Points" (2017). ETD Collection for University of Texas, El Paso. AAI10620057.