definemissingstatus

the name of the panel dataset in the long format with each row denoting a subject-visit observation
for ALL the planned visits, regardless of being missed or not. When a subject is lost to follow up, the data set
 must include the observation at the first time of being lost to follow up.

data

the name of the level-2 clustering variable.

the name of the variable denoting the time of the visit. Can set time=NULL if data is already sorted by id and time within 
id.

time

the name of the outcome variable of the interest that is subject to missingness.

Utility function to generate missing status variables in longitudinal data with dropout and/or intermittent missingness.

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>.

definemissingstatus: Utility function to generate missing status variables in longitudinal data with dropout and/or intermittent missingness.

Description

Usage

Arguments

Value

Examples