  Evaluating and tracking patients' functional status through the post-acute
care continuum requires a common instrument. However, different post-acute
service providers such as nursing homes, inpatient rehabilitation facilities
and home health agencies rely on different instruments to evaluate patients'
functional status. These instruments assess similar functional status domains,
but they comprise different activities, rating scales and scoring instructions.
These differences hinder the comparison of patients' assessments across health
care settings. We propose a two-step procedure that combines nested multiple
imputation with the multivariate ordinal probit (MVOP) model to obtain a common
patient assessment scale across the post-acute care continuum. Our procedure
imputes the unmeasured assessments at multiple assessment dates and enables
evaluation and comparison of the rates of functional improvement experienced by
patients treated in different health care settings using a common measure. To
generate multiple imputations of the unmeasured assessments using the MVOP
model, a likelihood-based approach that combines the EM algorithm and the
bootstrap method as well as a fully Bayesian approach using the data
augmentation algorithm are developed. Using a dataset on patients who suffered
a stroke, we simulate missing assessments and compare the MVOP model to
existing methods for imputing incomplete multivariate ordinal variables. We
show that, for all of the estimands considered, and in most of the experimental
conditions that were examined, the MVOP model appears to be superior. The
proposed procedure is then applied to patients who suffered a stroke and were
released from rehabilitation facilities either to skilled nursing facilities or
to their homes.
