arXiv subjects

Sandrine Boulet

Publications and source records attributed to Sandrine Boulet.

3 recordsLinked to original sources

arXiv2025

Sample size reassessment in Bayesian hybrid clinical trials

The use of historical controls offers a valuable alternative when traditional randomized controlled trials are not feasible. However, such approaches may introduce bias due to temporal changes in patient populations, diagnostic criteria, and/or treatment standards. Hybrid designs, which combine a concurrent control arm with historical control data, can help mitigate the possible bias. We propose a novel Bayesian two-arm randomized clinical trial design incorporating an interim analysis. At the interim analysis, a new criterion derived from the Hellinger distance is used to quantify the similarity between historical and concurrent control data outcomes. This measure informs both (1) the variance function of the control prior distribution in the final analysis and (2) the sample size reassessment for the second stage of the trial. The proposed approach is designed to accommodate both continuous and binary endpoints and is assessed through extensive simulation studies. Results demonstrate the method flexibility and robustness in adapting to varying degrees of historical-control heterogeneity.

stat.ME↗

arXiv2024

Straightforward Phase I Dose-Finding Design for Healthy Volunteers Accounting for Surrogate Activity Biomarkers

Conventionally, a first-in-human phase I trial in healthy volunteers aims to confirm the safety of a drug in humans. In such situations, volunteers should not suffer from any safety issues and simple algorithm-based dose-escalation schemes are often used. However, to avoid too many clinical trials in the future, it might be appealing to design these trials to accumulate information on the link between dose and efficacy/activity under strict safety constraints. Furthermore, an increasing number of molecules for which the increasing dose-activity curve reaches a plateau are emerging.In a phase I dose-finding trial context, our objective is to determine, under safety constraints, among a set of doses, the lowest dose whose probability of activity is closest to a given target. For this purpose, we propose a two-stage dose-finding design. The first stage is a typical algorithm dose escalation phase that can both check the safety of the doses and accumulate activity information. The second stage is a model-based dose-finding phase that involves selecting the best dose-activity model according to the plateau location.Our simulation study shows that our proposed method performs better than the common Bayesian logistic regression model in selecting the optimal dose.

stat.AP↗

arXiv2022

Bayesian Framework for Multi-Source Data Integration -- Application to Human Extrapolation From Preclinical Studies

In preclinical investigations, e.g. in in vitro, in vivo and in silico studies, the pharmacokinetic, pharmacodynamic and toxicological characteristics of a drug are evaluated before advancing to first-in-man trial. Usually, each study is analyzed independently and the human dose range does not leverage the knowledge gained from all studies. Taking into account the preclinical data through inferential procedures can be particularly interesting to obtain a more precise and reliable starting dose and dose range. We propose a Bayesian framework for multi-source data integration from preclinical studies results extrapolated to human, which allow to predict the quantities of interest (e.g. the minimum effective dose, the maximum tolerated dose, etc.) in humans. We build an approach, divided in four main steps, based on a sequential parameter estimation for each study, extrapolation to human, commensurability checking between posterior distributions and final information merging to increase the precision of estimation. The new framework is evaluated via an extensive simulation study, based on a real-life example in oncology inspired from the preclinical development of galunisertib. Our approach allows to better use all the information compared to a standard framework, reducing uncertainty in the predictions and potentially leading to a more efficient dose selection.

stat.ME↗