In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed timepoints. The focus of the trial may be to compare the mean outcome in two or more groups at some prespecified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The 'salbm' package allows the user to perform a (parameterized) sensitivity analysis of this assumption where the outcome of interest is binary (coded as 0, 1, or NA). In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. See the paper Daniel O. Scharfstein, Jaron J. R. Lee, Aidan McDermott, Aimee Campbell, Edward Nunes, Abigail G. Matthews, Ilya Shpitser "MarkovRestricted Analysis of Randomized Trials with NonMonotone Missing Binary Outcomes: Sensitivity Analysis and Identification Results" (2021) <arXiv:2105.08868>.
Package details 


Author  Daniel O. Scharfstein [aut], Aidan McDermott [aut, cre] 
Maintainer  Aidan McDermott <amcderm1@jhu.edu> 
License  GPL2 
Version  1.0 
Package repository  View on CRAN 
Installation 
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