There is high variability in response to cancer chemotherapies among patients. Its sources are diverse: genetic, physiologic, comorbidities, concomitant medications, environment, compliance, etc. As the therapeutic window of anticancer drugs is usually narrow, such variability may have serious consequences: severe (even life-threatening) toxicities or lack of therapeutic effect. Therefore, various approaches to individually tailor treatments and dosing regimens have been developed: a priori (based on genetic information, body size, drug elimination functions, etc.) and a posteriori (that is using information of measurements of drug exposure and/or effects). Mixed-effects modelling of pharmacokinetics and pharmacodynamics (PK-PD), combined with Bayesian maximum a posteriori probability estimation of individual effects, is the method of choice for a posteriori adjustments of dosing regimens. In this thesis, a novel approach to adjust the doses on the basis of predictions, given by a model for ordered categorical observations of toxicity, was developed and investigated by computer simulations. More technical aspects concerning the estimation of individual parameters were analysed to determine the factors of good performance of the method. These works were based on the example of capecitabine-induced hand-and-foot syndrome in the treatment of colorectal cancer. Moreover, a review of pharmacodynamic models for discrete data (categorical, count, time-to-event) was performed. Finally, PK-PD analyses of hydroxyurea in the treatment of sickle cell anemia were performed and used to compare different dosing regimens and determine the optimal measures for monitoring the treatment

A graphical model is simply a representation of the results of an analysis of relationships between sets of variables. It can include the study of the dependence of one variable, or a set of variables on another variable or sets of variables, and can be extended to include variables which could be considered as intermediate to the others. This leads to the concept of representing these chains of relationships by means of a graph; where variables are represented by vertices, and relationships between the variables are represented by edges. These edges can be either directed or undirected, depending upon the type of relationship being represented. The thesis investigates a number of outstanding problems in the area of statistical modelling, with particular emphasis on representing the results in terms of a graph. The thesis will study models for multivariate discrete data and in the case of binary responses, some theoretical results are given on the relationship between two common models. In the more general setting of multivariate discrete responses, a general class of models is studied and an approximation to the maximum likelihood estimates in these models is proposed. This thesis also addresses the problem of measurement errors. An investigation into the effect that measurement error has on sample size calculations is given with respect to a general measurement error specification in both linear and binary regression models. Finally, the thesis presents, in terms of a graphical model, a re-analysis of a set of childhood growth data, collected in South Wales during the 1970s. Within this analysis, a new technique is proposed that allows the calculation of derived variables under the assumption that the joint relationships between the variables are constant at each of the time points.

Il existe une grande variabilité dans la réponse aux chimiothérapies anticancéreuses. Ses sources sont diverses: génétiques, physiologiques, comorbidités, médicaments associés, etc. La marge thérapeutique de ces médicaments étant généralement étroite, une telle variabilité peut avoir de graves conséquences: toxicités graves ou absence d'effet thérapeutique. Plusieurs approches pour adapter individuellement les posologies ont été proposées: a priori (basées sur l'information génétique, la taille corporelle, les fonctions d'élimination, etc.) et a posteriori (sur les informations de mesures d'exposition au médicament et/ou effets). La modélisation à effets-mixtes de la pharmacocinétique et de la pharmacodynamie (PK-PD), combinée avec une estimation bayésienne des effets individuels, est la meilleure méthode pour individualiser des schémas posologiques a posteriori. Dans cette thèse, une nouvelle approche pour ajuster les doses sur la base des prédictions données par un modèle pour les observations catégorielles de toxicité a été développée et explorée par simulation. Les aspects plus techniques concernant l'estimation des paramètres individuels ont été analysés pour déterminer les facteurs de bonne performance de la méthode. Ces travaux étaient basés sur l'exemple du syndrome mains-pieds induit par la capécitabine dans le traitement du cancer colorectal. Une revue des modèles pharmacodynamiques de données discrètes (catégorielles, de comptage, de survie) a été effectuée. Enfin, des analyses PK-PD de l'hydroxyurée dans le traitement de la drépanocytose ont été réalisées pour comparer des différentes posologies et déterminer les modalités optimales de suivi du traitement / There is high variability in response to cancer chemotherapies among patients. Its sources are diverse: genetic, physiologic, comorbidities, concomitant medications, environment, compliance, etc. As the therapeutic window of anticancer drugs is usually narrow, such variability may have serious consequences: severe (even life-threatening) toxicities or lack of therapeutic effect. Therefore, various approaches to individually tailor treatments and dosing regimens have been developed: a priori (based on genetic information, body size, drug elimination functions, etc.) and a posteriori (that is using information of measurements of drug exposure and/or effects). Mixed-effects modelling of pharmacokinetics and pharmacodynamics (PK-PD), combined with Bayesian maximum a posteriori probability estimation of individual effects, is the method of choice for a posteriori adjustments of dosing regimens. In this thesis, a novel approach to adjust the doses on the basis of predictions, given by a model for ordered categorical observations of toxicity, was developed and investigated by computer simulations. More technical aspects concerning the estimation of individual parameters were analysed to determine the factors of good performance of the method. These works were based on the example of capecitabine-induced hand-and-foot syndrome in the treatment of colorectal cancer. Moreover, a review of pharmacodynamic models for discrete data (categorical, count, time-to-event) was performed. Finally, PK-PD analyses of hydroxyurea in the treatment of sickle cell anemia were performed and used to compare different dosing regimens and determine the optimal measures for monitoring the treatment