Simultaneous score confidence bounds for risk differences in multiple comparisons to a control

Asymptotic simultaneous lower (upper) confidence bounds for risk differences arising from comparing several treatments to a common control are constructed by inverting the maximum (minimum) of score statistics. With a few exceptions, these bounds perform better in terms of simultaneous coverage probability than procedures based on adjusted Wald methods (e.g., adding pseudo-observations), especially over relevant parts of the parameter space in superiority or inferiority studies. A further improvement is realized by using an appropriate multiplicity adjusted critical value that takes advantage of the correlation information in the score statistics estimated under the null instead of a regular plug-in estimate. Simulation results and a worked example show a gain in terms of the precision of the lower bounds and their power; however, not too much is lost when using the straightforward Sidak multiplicity adjustment when the number of comparisons is small. All methods discussed are implemented and reproducible with general and publicly available R code.