title: Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology.
abstract: Following the pattern of phase I clinical trials for cytotoxic drugs, dose-finding clinical trials in oncology of molecularly targeted agents (MTA) aim at determining the maximum tolerated dose (MTD). In classical phase I clinical trials, MTD is generally defined by the number of patients with short-term major treatment toxicities (usually called dose-limiting toxicities, DLT), occurring during the first cycle of study treatment (e.g. within the first 3weeks of treatment). However, S. Postel-Vinay (2011) highlighted that half of grade 3 to 4 toxicities, usually considered as DLT, occur after the first cycle of MTA treatment. In addition, MTAs could induce other moderate (e.g. grade 2) toxicities which could be taken into account depending on their clinical importance, chronic nature and duration. Ignoring these late toxicities may lead to an underestimation of the drug toxicity and to wrong dose recommendations for phase II and III clinical trials. Some methods have been proposed, such as the time-to-event continuous reassessment method (Cheung 2000 and Mauguen 2011), to take into account the late toxicities. We suggest approaches based on longitudinal models (Doussau 2013). We compare several models for longitudinal data, such as transitional or marginal models, to take into account all relevant toxicities occurring during the entire length of the patient treatment (and not just the events within a predefined short-term time-window). These models allow the statistician to benefit from a larger amount of safety data which could potentially improve that accuracy in MTD assessment.
authors.affiliation: Sanofi R&D, Biostatistique Oncologie, Vitry-sur-Seine, France.|UMR 518 MIA, AgroParisTech, Paris, 75005, France.
authors.forename: Pierre
authors.identifier: http://orcid.org/0000-0002-1388-4445
authors.initials: P
authors.lastname: Colin
authors.affiliation: Sanofi R&D, Biostatistique Oncologie, Vitry-sur-Seine, France.
authors.forename: Sandrine
authors.initials: S
authors.lastname: Micallef
authors.affiliation: UMR 518 MIA, AgroParisTech, Paris, 75005, France.|UMR 518 MIA, INRA, Paris, 75005, France.
authors.forename: Maud
authors.initials: M
authors.lastname: Delattre
authors.affiliation: Sanofi R&D, Biostatistique Oncologie, Vitry-sur-Seine, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Mancini
authors.affiliation: UMR 518 MIA, AgroParisTech, Paris, 75005, France.|UMR 518 MIA, INRA, Paris, 75005, France.
authors.forename: Eric
authors.initials: E
authors.lastname: Parent
mesh_terms: D000970:Antineoplastic Agents / Q000008:administration & dosage / Q000009:adverse effects; D017321:Clinical Trials, Phase I as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D000069451:Long Term Adverse Effects; D008390:Markov Chains; D020714:Maximum Tolerated Dose; D015233:Models, Statistical; D058990:Molecular Targeted Therapy / Q000009:adverse effects* / Q000706:statistics & numerical data*; D009369:Neoplasms / Q000188:drug therapy; D012107:Research Design; D012189:Retrospective Studies
chemical_list: D000970:Antineoplastic Agents
keywords: dose-finding; late toxicities; longitudinal data; oncology