title: Population modelling of patient responses in antidepressant studies: a stochastic approach.
abstract: This paper addresses the problem of modelling longitudinal data describing patients' responses in clinical trials. In particular, a systematic approach relying on a system theoretic paradigm is proposed to deal with contexts where limited physiopathological knowledge is available on disease, drug response, and patients' characteristics. The model relies on the notion of patient's health state which summarizes the patient's condition. In order to cope with the limited number of clinical data usually available, the paper considers a very parsimonious realization where the two state variables are the clinical endpoint and its derivative. Within a population framework, the individual response is modelled as the sum of an individual shift and the average response of subjects belonging to the same study, both described as Markovian processes and identified by empirical Bayes techniques. The proposed approach is validated with experimental data from a Phase II, flexible-dose, depression trial. The dose changes due to the flexible-dose scheme are handled as perturbations on the state. The connection between inter-individual variability and model stability is evaluated showing that the introduction of stable poles helps to describe populations whose range of individual responses does not diverge with time. In this way, good individual fittings and visual predictive checks were obtained for the clinical data. The proposed analysis provides a systematic approach to semi-mechanistic modelling when a precise knowledge of the physiological mechanisms of the disease is incomplete or missing.
authors.affiliation: University of Pavia, Via Ferrata 1, 27100 Pavia, Italy. Electronic address: eleonora.marostica@unipv.it.
authors.forename: Eleonora
authors.initials: E
authors.lastname: Marostica
authors.affiliation: Model-Based Drug Development, Janssen Research & Development, Beerse, Belgium.
authors.forename: Alberto
authors.initials: A
authors.lastname: Russu
authors.affiliation: PharmacoMetrica, La Fouillade, France.
authors.forename: Roberto
authors.initials: R
authors.lastname: Gomeni
authors.affiliation: Clinical Pharmacology Modeling & Simulation, GlaxoSmithKline, Stockley Park, UK.
authors.forename: Stefano
authors.initials: S
authors.lastname: Zamuner
authors.affiliation: University of Pavia, Via Ferrata 1, 27100 Pavia, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Nicolao
mesh_terms: D000928:Antidepressive Agents / Q000494:pharmacology*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003863:Depression / Q000188:drug therapy; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D055641:Mathematical Concepts; D015233:Models, Statistical; D013269:Stochastic Processes; D013598:Systems Theory
chemical_list: D000928:Antidepressive Agents
keywords: HAMD; Health state; Integrated random-walk; Population approach; Stochastic approach