In analyzing data collected in clinical research, the statistical models usually incorporate information provided by all the observations. The estimates, however, can rely on a small (possibly one) number of observations, illustrating their different influence. Individual influence measures have been proposed that also allow an improved understanding of the models to which they apply. The purpose of this work was to evaluate the individual influence in the setting of recent statistical models. The first section provides a measure of local influence in the proportional hazards model for the subdistribution function proposed by Fine and Gray to handle right censored data in the presence of competition. The second section of the work aims at highlighting the influence of the first individuals included in a dose finding trial (Phase I or II), using the Continual Reassessment Method (CRM). An adaptation of the CRM reducing the influence of first individuals is finally proposed.
