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Quinlan J.,Cytel, Inc | Gaydos B.,Eli Lilly and Company | MacA J.,Novartis | Krams M.,Pfizer
Clinical Trials | Year: 2010

Background This review discusses barriers to implementing adaptive designs in a pharmaceutical R&D environment and provides recommendations on how to overcome challenges. A summary of findings from a survey conducted through PhRMA's working group on adaptive designs is followed by a report based on our experience as statistical and clinical consultants to project teams charged with establishing the clinical development strategy for investigational compounds and interested in applying innovative approaches. Findings and recommendations Adaptive designs require additional work in that clinical trial simulations are needed to develop the design. Some project teams, due to time and resource constraints, are unable to invest the additional effort required to conduct necessary scenario analyses of options through simulation. We recommend formally integrating the planning time for scenario analyses and to incentivize optimal designs (e.g., designs offering the highest information value per resource unit invested). Regardless of the trial design ultimately chosen, quantitatively comparing alternative trial design options through simulation will enable earlier and better decision making in the context of the overall clinical development plan. Adhering to 'Good Adaptive Practices' will be key to achieving this goal. Outlook Implementing adaptive designs efficiently requires top-down and bottom- up support and the willingness to invest into integrated process and information technology infrastructures. Success is conditional on the willingness of the R&D environment to embrace the implementation of adaptive designs as a Change Management Initiative in the spirit of the Critical Path of the Food and Drug Administration. © 2010 The Author(s).

Antonijevic Z.,Cytel, Inc
Optimization of Pharmaceutical R and D Programs and Portfolios: Design and Investment Strategy | Year: 2015

Very little has been published on optimization of pharmaceutical portfolios. Moreover, most of published literature is coming from the commercial side, where probability of technical success (PoS) is treated as fixed, and not as a consequence of development strategy or design. In this book there is a strong focus on impact of study design on PoS and ultimately on the value of portfolio. Design options that are discussed in different chapters are dose-selection strategies, adaptive design and enrichment. Some development strategies that are discussed are indication sequencing, optimal number of programs and optimal decision criteria. This book includes chapters written by authors with very broad backgrounds including financial, clinical, statistical, decision sciences, commercial and regulatory. Many authors have long held executive positions and have been involved with decision making at a product or at a portfolio level. As such, it is expected that this book will attract a very broad audience, including decision makers in pharmaceutical R…D, commercial and financial departments. The intended audience also includes portfolio planners and managers, statisticians, decision scientists and clinicians. Early chapters describe approaches to portfolio optimization from big Pharma and Venture Capital standpoints. They have stronger focus on finances and processes. Later chapters present selected statistical and decision analysis methods for optimizing drug development programs and portfolios. Some methodological chapters are technical; however, with a few exceptions they require a relatively basic knowledge of statistics by a reader. © Springer International Publishing Switzerland 2015.

Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2010

The goal of this project is to develop a prototype software for some basic parametric and nonparametric multiple comparison procedures allowing the analysis of clinical trials data by multiple comparison procedures that guarantee strong control of the family wise error rate (FWER). Regulators at the FDA have specifically identifed the statistical handling of multiple endpoints in clinical trials as a integral component of the Critical Path Initiative, which is intended to speed the process from the discovery of new molecular entities to the delivery of safe and efficatious medical compounds to patients.

Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 112.08K | Year: 2010

DESCRIPTION (provided by applicant): The overall goal of our research is to develop and extend powerful exact statistical tools for testing genetic association, and to incorporate these methods into two existing, widely used software packages (Cytel Studio, SAS) that will serve the needs of data analysts in pharmaceuticals, genetic epidemiology and public health, and other fields which require a greater understanding of the genetic determinants of complex disease. The demand for these analytic tools is rising dramatically, as rapid progress in genotyping technology is making it easier and less costly to measure sampled subjects for ever larger numbers of genetic markers. Genetic association represents an observed correlation between an investigative genetic marker and some physical trait, and can be assessed using either traditional case-control or family-based study designs. In either case, there are compelling applications of permutation or exact statistical approaches that are computationally challenging, yet are simply unavailable in currently used software or are implemented in a manner that requires excessive memory or computation. The computational innovations developed for this project will fill this gap, significantly improving the efficiency and power of existing tools used for genetic association under both family-based and case-control designs. During Phase I, we will build a prototype computer program that includes (i) exact family-based tests for both biallelic and multiallelic markers, and (ii) a permutation procedure that simultaneously tests genetic association assuming various modes of inheritance (i.e., recessive, dominant, additive, or codominant). We will also investigate the feasibility of incorporating these procedures into a SAS PROC, complementing and extending currently implemented SAS JMP Genomics procedures for testing genetic association. As a part of Phase II, we will integrate our Phase I tools into Cytel's StatXact system and into the SAS JMP Genomics system as an external procedure. We will additionally (i) extend the exact family-based procedures to accommodate haplotype data, (ii) develop and implement algorithms for permutation approaches to large-scale screening experiments, (iii) incorporate exact versions of basic genetic epidemiologic procedures, and (iv) incorporate efficient Monte Carlo sampling tools to extend the usefulness of the exact procedures to larger data sets. PUBLIC HEALTH RELEVANCE: Rapid progress in genotyping technology is making it easier and less costly to identify increasingly large numbers of genetic markers from sampled humans. These markers can be used to identify new genes potentially associated with many complex diseases. This project will provide genetics researchers with more accurate and efficient statistical tools for analyzing data from these studies.

Gao P.,The Medicines Company | Liu L.,Cytel, Inc | Mehta C.,Cytel, Inc | Mehta C.,Harvard University
Biometrical Journal | Year: 2013

A method of testing for noninferiority followed by testing for superiority in an adaptive group sequential design is presented. The method permits a data-dependent increase in sample size without any inflation of type-1 error. Closed-form expressions for computing conditional power and the sample size required to achieve any desired conditional power are derived. A new statistical method for performing inference on the primary efficacy parameter is derived. The method is used to obtain the p-value, median-unbiased point estimate and confidence interval for the efficacy parameter. For normal endpoints with known variance, the coverage of the confidence interval is exact. In other settings, the coverage is exact for large samples. An illustrative example is provided in which the methods of testing and estimation are applied to an actual clinical trial of acute bacterial skin and skin-structure infection. The operating characteristics of the trial are obtained by simulation and demonstrate that the type-1 error is preserved, the point estimate is median unbiased, and the confidence interval provides exact coverage up to Monte Carlo accuracy. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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