2014

SFdS

Title: Regulatory considerations for Subgroup Analysis in Clinical Trials.

Authors: Gautier Paux.

Conference: Annual day of the French Society of Statistics’s (SFdS) Group Biopharmacy, Paris (France), 27 November 2014.

Invited talk.

Abstract:

ASA webinar

Title: From Sample Size Calculations to Clinical Trial Optimization.

Authors: Alex Dmitrienko, Gautier Paux.

Conference: American Statistical Association Biopharmaceutical section, web-based lecture, 04 December 2014.

Invited talk.

Abstract: It is well known that sample size determination is one of the key aspects of designing a new clinical trial. A standard sample size calculation approach generally pursues a simple goal of computing a quick estimate of the required number of patients or events. Closed-form solutions are normally preferred, which often forces the trial’s sponsor to make simplifying assumptions. The clinical scenario evaluation (CSE) framework introduced in Benda et al (2010) provides an extended version of this basic “one-dimensional” approach. Clinical scenario evaluation focuses on a broad “multi-dimensional” approach to a quantitative assessment of the operating characteristics of several candidate analysis methods under multiple candidate trial designs to arrive at a solution which is consistent with the trial’s clinical objectives and maximizes a relevant success criterion or utility function.

In this webinar we will introduce the key principles of clinical scenario evaluation in the context of Phase II and Phase III clinical trials and touch upon multiple related approaches. We will discuss the general concept of clinical trial optimization aimed at identifying the configurations of applicable design scenarios and analysis strategies that lead to optimal performance. We will also emphasize the importance of sensitivity assessments to ensure that an optimal clinical trial design is robust to reasonable deviations from the assumed parameter values.

The CSE approach will be illustrated using case studies based on a clinical trial with multiple objectives, clinical trial with a biomarker-driven design and an adaptive Phase II clinical trial. Finally, we will introduce an R package (Mediana package) which was developed to provide a general software implementation of the CSE approach. The current version of the Mediana package supports a broad set of clinical trial designs and analysis methods, and we will discuss new features that will be added in the future.

ERCIM

Title: Mediana: R package for power evaluation in clinical trials with multiplicity adjustment methods.

Authors: Gautier Paux, Alex Dmitrienko.

Conference: 7th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2014), Pisa (Italia), 08 December 2014.

Contributed talk - Session “Contributions to biostatistics and bioinformatics” chaired by Kim-Anh Do (Department Chair, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX).

Abstract: Clinical development programs and clinical trials should be designed to ensure that, if a treatment is effective, there is a high probability to detect an effect of a given size. In modern drug development, sponsors are interested in assessing the efficacy of a new treatment on multiple endpoints, evaluating multiple doses compared to a control, or determining a treatment effect in multiple subgroups. Due to this multiplicity of tests, the probability of erroneously claiming the effectiveness of a new drug, i.e. the Type I error rate, will be inflated and must be controlled to support reliable statistical inferences. In recent decades, multiple new methods for addressing multiplicity issues in clinical trials have been developed. In the context of clinical trials with multiple objectives, sample size and power calculations should reflect the multiple testing strategy to be used. However, general analytical expressions of the power function do not exist and power evaluation is simulation-based. The Mediana R package provides a general framework for the power evaluation in clinical trial with multiplicity issues. It is based on the Clinical Scenario Evaluation approach, allowing a quantitative assessment of operating characteristics of candidate designs and statistical methods to characterize their performance in multiple settings.

2015

Symposium on Early Phase Dose Finding Methodology

Title: Transitioning from algorithmic to adaptive model-based designs for Phase I dose-escalation trials in oncology at Servier.

Authors: Gautier Paux, Frederic Dubois.

Conference: Symposium on Early Phase Dose Finding Methodology, Paris (France), 16 April 2015.

Abstract: Traditional or modified 3+3 algorithmic designs have been widely used in Phase I dose-escalation studies in oncology at Servier. In the past decades, adaptive model-based designs have been gaining popularity with the development of the Continual Reassessment Method (CRM) (O’Quigley et al, 1990) and more recently with the Bayesian Logistic Regression Model (BLRM) (Neuenschwander et al, 2008). Transitioning from algorithmic to adaptive model-based designs at Servier was fraught with pitfalls but efforts have been made to put this right for the last two years. In this presentation, we will review our recent experience of implementing adaptive model-based dose-escalation Phase I studies in Oncology, from protocol development to reporting of dose recommendation. A real case study will be presented to emphasize the use of adaptive model-based designs compare to 3+3 algorithmic one. Adaptive model-based designs for Phase I dose-finding trials in oncology have now been adopted as a standard at Servier.

PSI

Title: Key Principles of Clinical Trial Simulations to Improve the Probability of Success in Late-Stage Trials.

Authors: Gautier Paux, Alex Dmitrienko.

Conference: PSI Annual Conference, London (United Kingdom), 12 May 2015.

Abstract: Confronted with the increasing cost, duration and failure rate of new drug development programs, the use of innovative trial designs and analysis strategies has considerably increased over the past decade. In this context, clinical trial simulations take a crucial and invaluable role to support a thorough assessment of the operating characteristics and performance of candidate designs and strategies. Before conducting a trial, simulation-based methods allow clinical trial sponsors to evaluate the effect of individual design or analysis parameters (as well as their synergic effect) on relevant criteria of trial success. Additionally, they facilitate the assessment of risks and benefits associated with each candidate design and analysis strategy and provide justification of parameter choices. Recently, the Mediana R package has been developed to provide a standardized approach to clinical trial simulations to facilitate a systematic simulation-based assessment of trial designs and analysis methods in clinical trials or across development programs. This package supports a broad range of trial designs and analysis methods typically used in late-stage trials. In this presentation we will discuss key principles of clinical trial simulations in the context of Phase II and Phase III trials to arrive at the optimal selection of design and analysis parameters

2016

TICTS

Title: Clinical trial optimization approaches to Phase III trials with multiple objectives.

Authors: Gautier Paux, Alex Dmitrienko.

Conference: Trends and Innovations in Clinical Trials Statistics, Durham (USA), 03 May 2016.

Invited talk - Session “Clinical Trial Optimization & Visualization” chaired by Jeff Maca (Quintiles)

Abstract: In modern drug development, clinical trial sponsors are interested in assessing the efficacy of a new treatment on multiple endpoints, evaluating multiple doses compared to a control, or determining a treatment effect in multiple subgroups. Due to this multiplicity of tests, the probability of erroneously claiming the effectiveness of a new drug, i.e. the Type I error rate, will be inflated and must be controlled to support reliable statistical inferences. In recent decades, several new methods for addressing multiplicity issues in clinical trials have been developed. In the context of clinical trials with multiple objectives, sample size and power calculations should reflect the multiple testing strategy to be used. Recently, the Mediana R package has been developed to provide a standardized approach to clinical trial simulations to facilitate a systematic simulation-based assessment of trial designs and analysis methods in clinical trials or across development programs. This package supports a broad range of trial designs and analysis methods typically used in late-stage trials. In this presentation we will discuss and illustrate key principles of clinical trial optimization in the context of late-phase trials with multiplicity issues to arrive at the optimal selection of design and analysis parameters.