The variables are as follows:<ul>
<li>y. number of days</li>
<li>time. in weeks</li>
<li>sub. patients id</li>
<li>group. treatment group with placebo (PBO) and desipramine (DMI)</li>
<li>basey. baseline days</li>
<li>g1. dummy variables for treatment group</li>
<li>g2. dummy variables for treatment group</li>
<li>time1. dummy variables for time</li>
<li>time2. dummy variables for time</li>
<li>grptime. group by time</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 linear mixed model for 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 more details, see Troxel Ma and Heitjan (2004) and Xie and Heijan (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>.

coc: A data set for Psychiatric Drug Treatment

Description

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