  Palliative medicine is an interdisciplinary specialty focusing on improving
quality of life (QOL) for patients with serious illness and their families.
Palliative care programs are available or under development at over 80% of
large US hospitals (300+ beds). Palliative care clinical trials present unique
analytic challenges relative to evaluating the palliative care treatment
efficacy which is to improve patients diminishing QOL as disease progresses
towards end of life (EOL). A unique feature of palliative care clinical trials
is that patients will experience decreasing QOL during the trial despite
potentially beneficial treatment. Often longitudinal QOL and survival data are
highly correlated which, in the face of censoring, makes it challenging to
properly analyze and interpret longitudinal QOL trajectory. To address these
issues, we propose a novel semiparametric statistical approach to jointly model
longitudinal QOL and survival data. There are two sub-models in our approach: a
semiparametric mixed effects model for longitudinal QOL and a Cox model for
survival. We use regression splines method to estimate the nonparametric curves
and AIC to select knots. We assess the model through simulation and application
to establish a novel modeling approach that could be applied in future
palliative care treatment research trials.
