Seminar
Susan Paddock
"Bayesian variable selection for analyzing longitudinal substance abuse treatment data
with informative censoring"
Measuring the process of care in substance abuse treatment requires analyzing repeated client
assessments at critical time points during treatment tenure. These assessments are frequently
censored due to early departure from treatment. Informative censoring is often characterized by
the last observed assessment time. However, if missing assessments for those who remain in
treatment are attributable to logistical reasons rather than to the underlying treatment process
being measured, then length of stay might better characterize censoring than would time of
measurement. In this talk, I will describe how to incorporate Bayesian variable selection into
the Conditional Linear Model to assess whether time of measurement or length of stay better
characterizes informative censoring while incorporating uncertainty about the effect of censoring
on treatment process change into the analysis.
