A 2-Stage Design Combining Both Group-Sequential and Sample Size Re-Estimation for a Slope Endpoint in a Small Sample Size Setting

Speaker: Yuqian Shen

Yuqian Shen is currently a Study Statistician at Sanofi in the area of rare disease. In her 3-year working experience in the pharmaceutical industry, her role is mainly on supporting statistical related activities in late phases of clinical development including a study with innovative design, and two successful global marketing registration submissions. Her research interests include adaptive designs, and the application of RWE in the clinical trial development in rare disease. She received her PhD in Statistics from Western Michigan University in 2019.

Abstract

Due to highly unmet medical needs in rare disease areas, there is great desire to speed up the drug development process. With extraordinary challenge in recruitment and many uncertainties, adaptive designs may be employed to trials in rare disease areas. A development option of a placebo-controlled registration study through a two-stage adaptive trial design is being evaluated. Stage 1 consists of participants in ongoing phase 2 study with 1-year double blind (DB) treatment and stage 2 includes newly enrolled participants with 2-year DB treatment period. The primary endpoint is the annualized rate of change (slope) for a continuous longitudinal measurement which will be evaluated through a linear mixed model. An unblinded interim analysis will be performed using Stage 1 data to re-estimate the sample size for Stage 2, followed by another interim analysis for potential early efficacy stopping when all participants completed the 1-year DB treatment. To control the overall type 1 error rate, rather than using a conservative approach, the actual correlation between the interim and final test statistics will be taken into account to determine the final significance level after pre-specifying the significance level for the interim efficacy analysis and the demonstration of the independent increment property for the slope analysis. Other factors such as different longitudinal timepoints collected for different participants as well as variability assumptions that may impact the type 1 error control and power for the slope analysis will also be discussed. Multiplicity adjustments for secondary endpoints at interim vs. final analysis will also be considered.

A CID Proposal for Somatrogon in a Single Master Protocol Across Three Indications

Speaker: Yuvika Paliwal

Yuvika Paliwal is currently a late phase statistician in the Rare Disease Statistics Group at Pfizer. She has over 7 years of experience as a statistician in both early and late phase clinical development across multiple therapeutic areas. She received her PhD in Biostatistics from University of Pittsburgh in 2017. Her research interests include the usage of adaptive designs and leveraging RWD in rare disease clinical trials.

Abstract

Daily recombinant hGH (rhGH) therapy has been proven safe and effective for the treatment of growth failure in children with SGA, TS, and ISS as well as other growth disorders including pediatric growth hormone deficiency (pGHD). Its use has been approved for these indications by the US FDA and other regulatory agencies worldwide.

Somatrogon, a long-acting recombinant human growth hormone (rhGH) reduces the current standard (SoC) of care of once daily injections to a once weekly administration regimen. We have conducted a clinical program, which has demonstrated non-inferiority (NI) of somatrogon compared to daily Genotropin for pGHD patients. We intend to study somatrogon in other conditions involving growth failure in children, specifically, SGA, TS, and ISS. All these conditions result in growth failure, albeit because of different etiologies. We proposed a single study utilizing a master protocol design that promotes efficiencies for the sponsor, investigators, study participants, and regulatory agencies and a novel application of the standard meta-analysis across the three indications. This single master protocol approach provides the framework by which we are able to evaluate a common intervention across multiple indications in parallel, which is an innovative alternative to the standard series of clinical trials that typically investigate one intervention in a disease in a single study.

The study design and statistical methodologies will be described. Simulations are also presented to compare study power under different sample size schemes and different analysis models.

A Confirmatory Basket Design Considering Non-inferiority and Superiority Testing

Speaker: Glen Laird & Yaohua Zhang

A practicing statistician for over 20 years, Glen Laird is currently the head of Biostatistics Methodology and Innovation at Vertex Pharmaceuticals, having previously led the GMA Biostatistics group at Vertex. Prior to his 5 years at Vertex, Glen worked in oncology biostatistics at Novartis, BMS, and Sanofi, assuming roles with increasing responsibility across early and full development. Glen graduated with a PhD in Statistics from Florida State University and worked as a survey statistician for RTI International before joining the pharmaceutical industry.

Dr. Yaohua Zhang is an Associate Director at Vertex Pharmaceuticals. Since joining Vertex in 2017, Yaohua has worked on various trials from phase I to phase III and submission work. Most recently, he is the project lead statistician of two disease areas and is actively engaging in strategic planning, early pipeline development and theoretical research. Yaohua graduated with a PhD degree in statistics from UConn in 2017 and since then has been an active alumnus. He has been supporting NESS activities and sit on several NESS committees. Apart from his daily busy work, he also conducts several research topics related to practical issues seen in the setting of pharmaceutical industry through collaboration with colleagues.

Abstract

For multiple rare diseases as defined by a common biomarker signature, or a disease with multiple disease subtypes of low frequency, it is often possible to provide confirmatory evidence for these disease or subtypes (baskets) as a combined group. A novel drug, as a second generation, may have marginal improvement in efficacy overall but superior efficacy in some baskets. In this situation, it is appealing to test hypotheses of both non-inferiority overall and superiority on certain baskets. The challenge is designing a confirmatory study efficient to address multiple questions in one trial. A two-stage adaptive design is proposed to test the non-inferiority hypothesis at the interim stage, followed by pruning and pooling before testing a superiority hypothesis at the final stage. Such a design enables an efficient and novel registration pathway, including an early claim of non-inferiority followed by a potential label extension with superiority on certain baskets and an improved benefit-risk profile demonstrated by longer term efficacy and safety data. Operating characteristics of this design are examined by simulation studies, and its appealing features make it ready for use in a confirmatory setting, especially in emerging markets, where both the need and the possibility for efficient use of resources may be the greatest.

A Roadmap to Using Historical Controls in Clinical Trials: Results from DIA-ADSWG

Speaker: Mercedeh Ghadessi

Mercedeh joined Bayer in 2015 in the research and early development statistics and currently moved to Oncology Medical Statistics at Bayer. She has a BSc in Applied Mathematics from Sharif University in Tehran/Iran, and an MSc in Applied Statistics focused on genetics from Simon Fraser University in Canada. Overall, she has 15 years of work experience in computer science and software development and has been working as a statistician for over 17 years in different biotech and pharma companies. Mercedeh is a Bayer science fellow who has several publications in different areas of her interest such as the use of historic controls in clinical trials and increasing the efficiency of oncology basket trials using Bayesian approaches. Recently, she co-authored a book on rare disease drug development with DIA colleagues.

Abstract

Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs.

Endpoint Identification and Validation for Generalized Pustular Psoriasis (GPP)

Speaker: Na Hu

Dr. Na Hu is currently working as Senior Principal Clinical Data Scientist at Boehringer Ingelheim, where she focuses on clinical development in immunology diseases area. Na received PhD in Biostatistics from University of Missouri at Columbia. Prior to joining BI in 2014, she worked in Novartis Oncology. Her main research interests are in survival analysis and adaptive design in clinical trials.

Federated Analytics for Timely Surrogate Endpoint Validation in Rare Diseases: An Application in AL Amyloidosis Combining Data across Institutional and National Boundaries

Speaker: James Signorovitch

James Signorovitch is a Partner at Analysis Group Inc., in Boston, where he advises life sciences companies on drug development, real-world evidence and market access, with a long-term commitment to problem solving for rare diseases.

Abstract

Data access is common hurdle for research that is needed to improve rare disease drug development. We encountered this hurdle when seeking to evaluate a biomarker, NT-proBNP, in AL amyloidosis. The necessary data from randomized trials resided at academic institutions in the European Union and China, and at different pharmaceutical manufacturers – data that could not be pooled in one place in the foreseeable future. To learn from these data without delay, we developed a federated analytics platform consisting of a common data model, pre-packaged analytics, and a process for engaging and supporting teams at each institution to run harmonized analyses locally before pooling the results into the final, global analysis. The study was conducted within the Amyloidosis Research Consortium (ARC), a patient-led, research-focused nonprofit organization, which brought together leading academic experts in AL amyloidosis, drug developers, representatives from the US and European regulatory authorities, and experts in data science and statistical methodologies, through a Public-Private Partnership between ARC and the US FDA, known as the Amyloidosis Forum. We report on the process we followed and the learning we derived, with the goal of assisting others in following a federated path to shared learning without waiting for shared data.