Development of dose-finding method based on a toxicity score for designs evaluating molecularly targeted therapies in oncology
type: Distribution
access URL: https://theses.hal.science/tel-00980725
format: HTML
issued: 2026-05-05T14:05:54.707659
modified: 2026-05-05T14:05:54.684834

test_moissonnage_selune
type: Agent

Development of dose-finding method based on a toxicity score for designs evaluating molecularly targeted therapies in oncology
type: Dataset
contact point: Organization
  fn: CCSD
keyword: toxicity-score
keyword: oncologie
keyword: sdvmheplife-sciences-q-biohuman-health-and-pathology
keyword: quasi-bernoulli
keyword: therapie-ciblee
keyword: methode-de-phase-i
keyword: qlcrm
keyword: phase-i-designs
keyword: targeted-therapies
keyword: theses
keyword: oncology
keyword: score-de-toxicite
keyword: crm
keyword: infoeu-reposemanticsdoctoralthesis
landing page: https://theses.hal.science/tel-00980725
description: The aim of a phase I oncology trial is to identify a dose with an acceptable safety level. Most phase I designs use the Dose-Limiting Toxicity (DLT), a binary endpoint, to assess the level of toxicity. DLT might be an incomplete endpoint for investigating molecularly targeted therapies as a lot of useful toxicity information is discarded.In this work, we propose a quasi-continuous toxicity score, the Total Toxicity Profile (TTP), to measure quantitatively and comprehensively the overall burden of multiple toxicities. The TTP is defined as the Euclidean norm of the weights of toxicities experienced by a patient, where the weights reflect the relative clinical importance of each type and grade of toxicity.We propose then a dose-finding design, the Quasi-Likelihood Continual Reassessment Method (QLCRM), incorporating the TTP-score into the CRM, with a logistic model for the dose-toxicity relationship in a frequentist framework. Using simulations, we compare our design to three existing designs for quasi-continuous toxicity scores: i) the QCRM design, proposed by Yuan et al., with an empiric model for the dose-toxicity relationship in a Bayesian framework, ii) the UA design of Ivanova and Kim derived from the "up-and-down" methods for the dose-escalation process and using an isotonic regression to estimate the recommended dose at the end of the trial, and iii) the EID design of Chen et al. using the isotonic regression for the dose-escalation process and for the identification of the recommended dose.We also perform a simulation study to evaluate the TTP-driven methods in comparison to the classical DLT-driven CRM. We then evaluate the robustness of these designs in a setting where grades can be misclassified.In the last part of this work, we illustrate the process of building the TTP-score and the application of the QLCRM method through the example of a paediatric trial. In this study, we have used the Delphi method to elicit the weights and the target toxicity-score c…
identifier: NNT: 2013PA11T060
issued: 2026-05-05T14:05:54.704053
language: fr
modified: 2026-05-05T14:05:54.704058