skquit

The variables are as follows:<ul>
<li>id. subjects id</li>
<li>time. Time in weeks after baseline</li>
<li>quit. 0: smoking; 1: quit smoking</li>
<li>sub. patients id</li>
<li>helmert1. Herlmert contrast 1 among 4 treatment groups.</li>
<li>helmert2. Herlmert contrast 2 among 4 treatment groups.</li>
<li>helmert3. Herlmert contrast 3 among 4 treatment groups.</li>
</ul>

datasets

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>.

skquit: A randomzied trial data set for Smoking cessation

Description

Usage

Arguments

Format