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Model based dose escalation: in practice

Abstract: At AstraZeneca, the implementation of a model based Bayesian dose escalation design in Oncology phase 1 studies has been facilitated by putting practical considerations in place that address the operational challenges that arise due to increased statistical complexity that accompanies the methodology. We will discuss a practical case study, describing the first time that this approach was implemented at AstraZeneca, including the options explored for specifying the prior, the advantages of the approach together with practical and methodological learnings for future studies.  The extension to 2-drug combination dose escalation studies will be introduced.

Abstract: Methods for finding the highest dose that has an acceptable risk of toxicity in Phase I dose-escalation clinical trials assuming a monotonic dose-response relationship have been studied extensively in recent decades. The assumption of monotonicity is fundamental in these methods. As a result, such designs fail to identify the correct dose when the dose-response relationship is non-monotonic, for example, in dose-schedules and dose-combinations trials. We propose a dose-escalation method that does not require monotonicity or any pre-specified relationship between dose levels. The design is based on the Bayesian model incorporating an information-theoretic criterion for dose selections. We will discuss the practical aspects of the design such as prior and safety stopping. For small sample size typical for dose-escalation studies we will show in simulations that the proposed method is comparable to well-studied and used methods under the assumption of monotonicity and outperforms them when this assumption is violated. We conclude with an extension of the design which allows to include an efficacy endpoint.

Biography: Pavel did his undergraduate studies in the National Research University Higher School of Economics, Moscow, Russia. He worked on information-theoretic aspects in statistics in the International Laboratory of Stochastic Analysis. Currently, Pavel is a PhD student in Lancaster University where he works on the project “Dose finding for combination trials with many treatments” under supervision of Professor Thomas Jaki. Pavel is a Marie-Curie Fellow in the IDEAS network. The aim of his project is to develop novel dose finding designs which do not require parametric assumptions and can be applied to various clinical trials. 

Abstract: Clinical trials with multiple strata (e.g. indication, dose regimen, mutation status) are increasingly used in drug development. In dose escalation trials, where data are often sparse and a multiple strata analysis may be the only option to study a new treatment, good statistical inference and decision-making can be challenging. Inferences from simple pooling or stratification are known to be inferior to hierarchical modeling methods, which build on exchangeable strata parameters to allow borrowing information across strata. However, the standard exchangeability (EX) assumption bears the risk of too much shrinkage and excessive borrowing for extreme strata. This talk will present the exchangeability-nonexchangeability (EXNEX) approach in dose finding studies, as a robust extension of the standard EX approach. It allows each stratum-specific parameter to be exchangeable with other similar strata parameters or nonexchangeable with any of them. A case study from a Phase I trial will also be shown.

Biography: After the degree in Statistics for biomedicine, environment and technology from the university of Rome “La Sapienza” (2008), Alessandro joined the pharmaceutical industry (Novartis). He is currently working as Associate Director of Biostatistics in the area of Phase I/II Oncology clinical trials in Early Development Biostatistics (EDB) group. Prior to join the EDB Oncology group Alessandro has worked as trainee in the CIS-Methodology Group, supporting the methodological development and implementation of oncology Bayesian single agent/combination dose-finding study design.

Abstract: The use of assurance to quantify the probability of success of a proposed parallel group clinical trial is well understood. This framework extends naturally to studies with other designs; including when evaluating dose response is the primary focus. Prior distributions formally elicited about the mean response at certain dose levels can be used to set certain constraints such that a spread of plausible three parameter E-max curves may be generated. In this presentation, we will discuss our experiences in the development of a dose response trial simulator. We illustrate how such an approach may be used to help characterise interim futility rules, dose adaptation and assurance.

Biography: Doug Thompson has been at GlaxoSmithKline for over three years working on the design, analysis and reporting of clinical trials in rare diseases. He studied statistics at the University of Glasgow as an undergraduate before going on to undertake a PhD at the University of Edinburgh on the development of a risk stratified approach for the treatment of acute stroke patients, funded through the MRC Hubs for Trials Methodology Research.

Abstract: Response to treatments is often highly heterogeneous, especially ones that are targeted at specific biological pathways. The increasing availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. Often new treatments are targeted at a specific biomarker subgroup, but may in fact work in a narrower or broader set of patients.

I will discuss Bayesian adaptive methodology for trials that have multiple treatments and biomarkers. The proposed design incorporates biological hypotheses about the links between treatments and biomarker subgroups, but allows alternative links to be formed during the trial. The statistical properties of the method compare well to alternative designs available.  This design has been developed for trials in ovarian cancer and breast cancer and some methodology issues specific to each application will be discussed. These include the use of continuous biomarker information to allocate patients and adding in new treatments and biomarkers during the trial.

Biography: James Wason is a Professor of Biostatistics at Newcastle University and a Programme Leader at the MRC Biostatistics Unit. He leads a research group focused on development of statistical methods for more efficient trials. His research interests are adaptive designs, using biomarkers prospectively during trials and analysis of composite endpoints. As well as working on methodology, James is very interested in applying novel methods in real clinical trials and is currently involved in several ongoing trials using novel designs.

Biography: Trevor has been the Group Lead for Early Phase Neuroscience Statistics at Eli Lilly since September 2011 covering neurodegeneration, psychiatric illnesses and pain.  This covers the clinical planning after Candidate Identification through to the decision to proceed to Phase 3.  Prior to this he was at Pfizer in Kent from 1998, where he worked on early clinical development as well.  Much of the work was in pain and many biomarker and methodology studies including: fMRI, other imaging biomarkers, healthy volunteer challenge models and patient biomarkers. Trevor has also worked for BioSS in Scotland and at Rothamsted.