Index of Local Sensitivity to Nonignorability
Utility function to generate missing status variables in longitudinal ...
Function for ISNI computation when the outcome follows GLMs.
Function for ISNI computation when the longitudinal/clustered binary o...
Function for ISNI computation when the outcome follows LMM.
Function for ISNI computation when the outcome follows marginal multiv...
Function to print the isniglm object.
Function to print the isniglmm object.
Function to print the isnilmm object.
Function to print the isnimgm object.
Function to print out a summary of isniglm object in a matrix form.
Function to print out a summary of isniglmm object in a matrix form.
Function to print out a summary of isnilmm object in a matrix form.
Function to print out a summary of isnimgm object in a matrix form.
Function to fit the transitional missing data model and obtain the pre...
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random is with respect to the assumption of ignorability. For a tutorial, download at <https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>. For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and Xie (2008) <doi:10.1002/sim.3117> and Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.