Statistical Methods In Drug Combination Studies

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Statistical Methods In Drug Combination Studies


Statistical Methods In Drug Combination Studies
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Statistical Methods In Drug Combination Studies


Statistical Methods In Drug Combination Studies
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Author : Wei Zhao
language : en
Publisher: CRC Press
Release Date : 2014-12-19

Statistical Methods In Drug Combination Studies written by Wei Zhao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-19 with Mathematics categories.


The growing interest in using combination drugs to treat various complex diseases has spawned the development of many novel statistical methodologies. The theoretical development, coupled with advances in statistical computing, makes it possible to apply these emerging statistical methods in in vitro and in vivo drug combination assessments. However, despite these advances, no book has served as a single source of information for statistical methods in drug combination research, nor has there been any guidance for experimental strategies. Statistical Methods in Drug Combination Studies fills that gap, covering all aspects of drug combination research, from designing in vitro drug combination studies to analyzing clinical trial data. Featuring contributions from researchers in industry, academia, and regulatory agencies, this comprehensive reference: Describes statistical models used to characterize dose–response patterns of monotherapies and evaluate the combination drug synergy Offers guidance for estimating interaction indices and constructing their associated confidence intervals to assess drug interaction Introduces a practical and innovative Bayesian approach to Phase I cancer trials, including actual trial examples to illustrate use Examines strategies in the fixed-dose combination therapy clinical development via case studies stemming from regulatory reviews Evaluates computational tools and software packages used to apply novel statistical methods in combination drug development Statistical Methods in Drug Combination Studies provides researchers with a solid understanding of the available statistical methods and computational tools and how to apply them in drug combination studies. The book is equally useful for statisticians to become better equipped to deal with drug combination study design and analysis in their practice.

Evaluating Synergy


Evaluating Synergy
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Author : Ming Tan
language : en
Publisher: Wiley
Release Date : 2015-10-19

Evaluating Synergy written by Ming Tan and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-19 with Medical categories.


Containing the historical and statistical information necessary to choose an analysis method and successful drug combination, Evaluating Synergy provides a systematic introduction of statistical methods for optimally designing and analyzing combination studies in cancer, anti-viral, and other therapeutic areas. This practical guide provides scientists in translational research, data analysts, and statisticians in cancer research with a detailed discussion on the challenging case of three or multi-drug combinations. Numerous examples accompany a presentation that illustrates experimental design considerations for modern drug analysis.

Bayesian Analysis With R For Drug Development


Bayesian Analysis With R For Drug Development
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Author : Harry Yang
language : en
Publisher: CRC Press
Release Date : 2019-06-26

Bayesian Analysis With R For Drug Development written by Harry Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-26 with Mathematics categories.


Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development. Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical, clinical, and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.

Handbook Of Methods For Designing Monitoring And Analyzing Dose Finding Trials


Handbook Of Methods For Designing Monitoring And Analyzing Dose Finding Trials
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Author : John O'Quigley
language : en
Publisher: CRC Press
Release Date : 2017-04-27

Handbook Of Methods For Designing Monitoring And Analyzing Dose Finding Trials written by John O'Quigley and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-27 with Mathematics categories.


Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials gives a thorough presentation of state-of-the-art methods for early phase clinical trials. The methodology of clinical trials has advanced greatly over the last 20 years and, arguably, nowhere greater than that of early phase studies. The need to accelerate drug development in a rapidly evolving context of targeted therapies, immunotherapy, combination treatments and complex group structures has provided the stimulus to these advances. Typically, we deal with very small samples, sequential methods that need to be efficient, while, at the same time adhering to ethical principles due to the involvement of human subjects. Statistical inference is difficult since the standard techniques of maximum likelihood do not usually apply as a result of model misspecification and parameter estimates lying on the boundary of the parameter space. Bayesian methods play an important part in overcoming these difficulties, but nonetheless, require special consideration in this particular context. The purpose of this handbook is to provide an expanded summary of the field as it stands and also, through discussion, provide insights into the thinking of leaders in the field as to the potential developments of the years ahead. With this goal in mind we present: An introduction to the field for graduate students and novices A basis for more established researchers from which to build A collection of material for an advanced course in early phase clinical trials A comprehensive guide to available methodology for practicing statisticians on the design and analysis of dose-finding experiments An extensive guide for the multiple comparison and modeling (MCP-Mod) dose-finding approach, adaptive two-stage designs for dose finding, as well as dose–time–response models and multiple testing in the context of confirmatory dose-finding studies. John O’Quigley is a professor of mathematics and research director at the French National Institute for Health and Medical Research based at the Faculty of Mathematics, University Pierre and Marie Curie in Paris, France. He is author of Proportional Hazards Regression and has published extensively in the field of dose finding. Alexia Iasonos is an associate attending biostatistician at the Memorial Sloan Kettering Cancer Center in New York. She has over one hundred publications in the leading statistical and clinical journals on the methodology and design of early phase clinical trials. Dr. Iasonos has wide experience in the actual implementation of model based early phase trials and has given courses in scientific meetings internationally. Björn Bornkamp is a statistical methodologist at Novartis in Basel, Switzerland, researching and implementing dose-finding designs in Phase II clinical trials. He is one of the co-developers of the MCP-Mod methodology for dose finding and main author of the DoseFinding R package. He has published numerous papers on dose finding, nonlinear models and Bayesian statistics, and in 2013 won the Royal Statistical Society award for statistical excellence in the pharmaceutical industry.

Bayesian Adaptive Methods For Phase I Clinical Trials


Bayesian Adaptive Methods For Phase I Clinical Trials
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Author : Ruitao Lin
language : en
Publisher:
Release Date : 2017-01-26

Bayesian Adaptive Methods For Phase I Clinical Trials written by Ruitao Lin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.


This dissertation, "Bayesian Adaptive Methods for Phase I Clinical Trials" by Ruitao, Lin, 林瑞涛, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The primary objective of phase I dose-finding trials is to determine the maximum tolerated dose (MTD), which is typically defined as the dose with the dose-limiting toxicity probability closest to the target toxicity probability. The American Society of Clinical Oncology (ASCO) recently published an update of the ASCO policy statement to call for new phase I trial designs to allow for more efficient escalation to the therapeutic dose levels in order to cope with the changing landscape in cancer research. In this thesis, innovative and robust designs for single- or multiple-agent phase I dose-finding trials are studied. To enhance robustness and efficiency, two nonparametric methods are proposed to locate the MTD in single-agent phase I clinical trials without imposing any parametric assumption on the underlying distribution of the toxicity curve. First, a uniformly most powerful Bayesian interval (UMPBI) design is developed for dose finding, where the optimal interval is determined by the rejection region of the uniformly most powerful Bayesian test. UMPBI is easy to implement and can be nicely interpreted. Compared with existing interval designs, the proposed UMPBI design exhibits a unique feature of convergence to the MTD. Next, a nonparametric overdose control (NOC) method is proposed by casting dose finding in a Bayesian model selection framework. Each dose assignment under NOC is determined such that the posterior probability of overdosing is controlled. In addition, NOC is incorporated with a fractional imputation method to deal with late-onset toxicity outcomes. Both of the UMPBI and NOC designs are flexible, as well as possessing sound theoretical support and desirable numerical performance. In the era of precision medicine, combination therapy is playing an increasingly important role in drug development. However, drug combinations often lead to a high-dimensional dose searching space compared to conventional single-agent dose finding, especially when three or more drugs are combined for treatment. Most of the current dose-finding designs aim to quantify the toxicity probability space using certain prespecified yet complicated models. Not only do these models characterize each individual drug's toxicity profile, but they also need to quantify their interaction effects, which often leads to multi-parameter models. In order to stabilize the current practice of dose finding in drug-combination trials with limited sample sizes, a random walk Bayesian optimal interval (RW-BOIN) design and a Bootstrap aggregating continual reassessment method (Bagging CRM) are proposed. RW-BOIN is built on the basis of the single-agent BOIN design, and it can be utilized to tackle high-dimensional dose-finding problems. A convergence theorem is established to ensure the validity of RW-BOIN. On the other hand, Bagging CRM implements a dimension reduction technique and some ensemble methods in machine learning, so that the toxicity probability space can be stably reduced to a one-dimensional searching line. Simulation studies show that both RW-BOIN and Bagging CRM have comparative and robust operating characteristics compared with existing approaches under various dose-toxicity scenarios. All of the proposed methods are exemplified with real phase I dose-finding trials. Subjects: Bayesian statistical decision theory Clinical trials - Statistical methods

Bayesian Methods In Pharmaceutical Research


Bayesian Methods In Pharmaceutical Research
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Author : Emmanuel Lesaffre
language : en
Publisher: CRC Press
Release Date : 2020-04-15

Bayesian Methods In Pharmaceutical Research written by Emmanuel Lesaffre and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-15 with Medical categories.


Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics, and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities, academia, and pharmaceutical industry, with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers: Theory, methods, applications, and computing Bayesian biostatistics for clinical innovative designs Adding value with Real World Evidence Opportunities for rare, orphan diseases, and pediatric development Applied Bayesian biostatistics in manufacturing Decision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research, development, and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods, applications, and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research.

Escalation With Overdose Control For Phase I Drug Combination Trials


Escalation With Overdose Control For Phase I Drug Combination Trials
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Author : Yun Shi
language : en
Publisher:
Release Date : 2017-01-26

Escalation With Overdose Control For Phase I Drug Combination Trials written by Yun Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.


This dissertation, "Escalation With Overdose Control for Phase I Drug-combination Trials" by Yun, Shi, 施昀, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The escalation with overdose control (EWOC) method is a popular modelbased dose finding design for phase I clinical trials. Dose finding for combined drugs has grown rapidly in oncology drug development. A two-dimensional EWOC design is proposed for dose finding with two agents in combination based on a four-parameter logistic regression model. During trial conduct, the posterior distribution of the maximum tolerated dose (MTD) combination is updated continuously in order to find the appropriate dose combination for each cohort of patients. The probability that the next dose combination exceeds the MTD combination can be controlled by a feasibility bound, which is based on a prespecified quantile for the MTD distribution such as to reduce the possibility of over-dosing. Dose escalation, de-escalation or staying at the same doses is determined by searching the MTD combination along rows and columns in a two-drug combination matrix. Simulation studies are conducted to examine the performance of the design under various practical scenarios, and illustrate it with a trial example. DOI: 10.5353/th_b4979973 Subjects: Clinical trials - Statistical methods Pharmaceutical arithmetic Drugs - Design Drug development