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 s tudies on healing processes in which, due to recovery, the status of patien ts only improves or remains the same. We show how this prior information ca n be used to construct appropriate and parsimoniously parametrized marginal models. As a second aspect, we also incorporate nonparametric fitting of c ovariate effects via a penalized quasi-likelihood or general estimating equ ation approach. We illustrate our methods by an application to sports-relat ed injuries.