Analysis of interval‐grouped recurrent‐event data using piecewise constant rate functions

We consider situations where subjects in a longitudinal study experience recurrent events. However, the events are observed only in the form of counts for intervals which can vary across subjects. Methods for estimating the mean and rate functions of the recurrent-event processes are presented, based on loglinear regression models which incorporate piecewise-constant baseline rate functions. Robust methods and methods based on mixed Poisson processes are compared in a simulation study and in an example involving superficial bladder tumours in humans. Both approaches provide a simple and effective way to deal with interval-grouped data. Nous considerons des situations ou les sujets d'une etude longitudinale font l'experience d'evenements recurrents. Cependant, les evenements sont observes seulement sous forme de compte des intervalles qui peuvent varier selon les sujets (i.e. Thall et Lachin 1988, Sun et Kalbfleisch 1993, Byar 1980). Des methodes pour estimer les fonctions de moyenne et de taux des procedes d'evenements recurrents sont presentees, basees sur des modeles de regression log-lineaire qui incorporent des fonctions de taux repere constantes par morceau. Des methodes robustes et des methodes fondees sur des precedes de Poisson melanges sont comparees dans une etude de simulation et dans un exemple impliquant des tumeurs superficielles de la vessie chez des humains. Les deux approches fournissent une facon simple et efficace de traiter les donnees groupees par intervalle.

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