Data from the Stanford Heart Transplantation Study and our own study on brain tumor include time-dependent covariates like transplantation, which may switch only once, and others changing their value several times during follow-up. But classical analyses never used this additional information. In a comparative study we applied the time-dependent Cox model, pooled Cox regression and the linear counting process by Aalen to these data sets. All methods do show similar results when they are carried out in their 'fixed' version, i.e. using baseline information only, or when covariates are being treated as time-dependent. But the estimated effects do differ remarkably between fixed and time-dependent approaches, thus leading to different interpretations of risks
