An application of isotonic longitudinal marginal regression to monitoring the healing process

This paper discusses marginal regression for repeated ordinal
measurements that are isotonic over time. Such data are often
observed in longitudinal studies on healing processes where, due to
recovery, the status of patients only improves or stays the same.
We show how this prior information can be used to construct
appropriate and parsimoniously parametrized marginal models. As a
second aspect, we also incorporate nonparametric fitting of
covariate effects via a penalized quasi-likelihood or GEE approach.
We illustrate our methods by an application to injuries from
sporting activities.