Level: Intermediate
Hadley Myers, JMP Systems Engineer, SAS
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
The need to determine confidence intervals for linear combinations of random mixed-model variance components, especially critical in Pharmaceutical and Life Science applications, was addressed with the creation of a JMP Add-In, demonstrated at the JMP Discovery Summit Europe 2020 and available at the JMP User Community. The add-in used parametric bootstrapping of the sample variance components to generate a table of simulated values and calculated “bias-corrected” (BC) percentile intervals on those values. BC percentile intervals are better in accounting for asymmetry in simulated distributions than standard percentile intervals, and a simulation study using a sample data set at the time showed closer-to-true α-values with the former. This work reports on the release of Version 2 of the Add-In, which calculates both sets of confidence intervals (standard and BC percentiles), as well as a third set, the “bias-corrected and accelerated” confidence interval, which has the advantage of adjusting for underlying higher-order effects. Users will therefore have the flexibility to decide for themselves the appropriate method for their data. The new version of the Add-In will be demonstrated, and an overview of the advantages/disadvantages of each method will be addressed.
