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| Author | : S. W. Bergman |
| Publisher | : CRC Press |
| Total Pages | : 270 |
| Release | : 2020-10-28 |
| Genre | : Mathematics |
| ISBN | : 1000105520 |
This book focuses on statistical methods which impinge more or less directly on the decisions that are made during the course of pharmaceutical and agro-chemical research, considering the four decision-making areas.
| Author | : S. W. Bergman |
| Publisher | : CRC Press |
| Total Pages | : 273 |
| Release | : 2020-10-29 |
| Genre | : Mathematics |
| ISBN | : 1000148734 |
This book focuses on statistical methods which impinge more or less directly on the decisions that are made during the course of pharmaceutical and agro-chemical research, considering the four decision-making areas.
| Author | : Frederick W. Faltin |
| Publisher | : John Wiley & Sons |
| Total Pages | : 533 |
| Release | : 2012-07-24 |
| Genre | : Medical |
| ISBN | : 1119942047 |
Statistical Methods in Healthcare In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed. With a focus on finding solutions to these challenges, this book: Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development. Offers a broad coverage of standards and established methods through leading edge techniques. Uses an integrated case study based approach, with focus on applications. Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance. Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes. Addresses the use of modern statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision. Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.
| Author | : Institute of Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 221 |
| Release | : 2001-01-01 |
| Genre | : Medical |
| ISBN | : 0309171148 |
Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.
| Author | : Philip Rowe |
| Publisher | : John Wiley & Sons |
| Total Pages | : 431 |
| Release | : 2015-07-20 |
| Genre | : Medical |
| ISBN | : 1118913418 |
Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need. This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science. This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research. a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences all examples set in relevant pharmaceutical contexts. key points emphasised in summary boxes and warnings of potential abuses in ‘pirate boxes’. supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab – provided at: https://www.wiley.com/go/rowe/statspharmascience2e An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
| Author | : Anand M. Joglekar |
| Publisher | : John Wiley & Sons |
| Total Pages | : 339 |
| Release | : 2003-09-19 |
| Genre | : Science |
| ISBN | : 0471465372 |
A guide to achieving business successes through statistical methods Statistical methods are a key ingredient in providing data-based guidance to research and development as well as to manufacturing. Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on-target performance. Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry. Emphasizing practical learning, applications, and performance improvement, Dr. Joglekar?s text shows today?s industry professionals how to: Summarize and interpret data to make decisions Determine the amount of data to collect Compare product and process designs Build equations relating inputs and outputs Establish specifications and validate processes Reduce risk and cost-of-process control Quantify and reduce economic loss due to variability Estimate process capability and plan process improvements Identify key causes and their contributions to variability Analyze and improve measurement systems This long-awaited guide for students and professionals in research, development, quality, and manufacturing does not presume any prior knowledge of statistics. It covers a large number of useful statistical methods compactly, in a language and depth necessary to make successful applications. Statistical methods in this book include: variance components analysis, variance transmission analysis, risk-based control charts, capability and performance indices, quality planning, regression analysis, comparative experiments, descriptive statistics, sample size determination, confidence intervals, tolerance intervals, and measurement systems analysis. The book also contains a wealth of case studies and examples, and features a unique test to evaluate the reader?s understanding of the subject.
| Author | : Mark Chang |
| Publisher | : CRC Press |
| Total Pages | : 255 |
| Release | : 2019-03-20 |
| Genre | : Mathematics |
| ISBN | : 1351214527 |
"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development.... Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.
| Author | : Subba Bayya Rao |
| Publisher | : Pharmamed Press |
| Total Pages | : 0 |
| Release | : 2023-05 |
| Genre | : Medical |
| ISBN | : 9789395039345 |
"Pharmaceutical Research Methodology and Bio-Statistics: Theory and Practice" is aimed in understanding the fundamental concepts of developing a research bent of mind by careful planning, execution, collection of data and analyzing for statistical significance. The book is aimed at B. Pharm, Pharm D, Pharm D (PB), M. Pharm, allied course students, researchers at the academic and industry levels, Ph. D scholars, policy makers, regulators etc. Key Features: - Distinguishing statistics and bio-statistics - How to identify a problem, plan for research and execute the idea - Chemical abstract literature search - Anatomy of a research paper - Compare and contrast of research proposal, research report, research paper, patent document, synopsis - Concept of meta-analysis to resolve research ambiguities - Data collection, cleansing, presenting - How to overcome missing data - Introduction to Probability, Permutations and Combinations - Parametric distributions - binomial, poisson, normal, chi-square, student 't', F distributions - Extra information on Bernoulli Distribution and Chebyshev's Theorem - Role of Type I and Type II errors, Power, sample size, confidence level, confidence interval, confidence limits - How to judge whether data upon analysis is statistical significant or not - Developing hypothesis as null, alternate and how to draw conclusion after conducting suitable statistical test - Non-parametric statistical test - Run, Sign, Wilcoxon Signed rank, Wilcoxon rank sum tests - Parametric, Non-parametric ANOVAs (1-way with multiple comparisons, 2-way, cross over, 3-way) - Step wise Parametric and non-parametric problem solving - Applications of linear regression and correlation coef ficient relating to pharmaceuticals - Appended with Multi-linear Regression Analysis (Mathematical and Excel Calculation)
| Author | : Thomas D. Cook |
| Publisher | : CRC Press |
| Total Pages | : 465 |
| Release | : 2007-11-19 |
| Genre | : Mathematics |
| ISBN | : 1584880279 |
Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial. After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals. Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.
| Author | : Robert H. Riffenburgh |
| Publisher | : Academic Press |
| Total Pages | : 680 |
| Release | : 2006 |
| Genre | : Business & Economics |
| ISBN | : |
Medicine deals with treatments that work often but not always, so treatment success must be based on probability. Statistical methods lift medical research from the anecdotal to measured levels of probability. This book presents the common statistical methods used in 90% of medical research, along with the underlying basics, in two parts: a textbook section for use by students in health care training programs, e.g., medical schools or residency training, and a reference section for use by practicing clinicians in reading medical literature and performing their own research. The book does not require a significant level of mathematical knowledge and couches the methods in multiple examples drawn from clinical medicine, giving it applicable context. Easy-to-follow format incorporates medical examples, step-by-step methods, and check yourself exercises Two-part design features course material and a professional reference section Chapter summaries provide a review of formulas, method algorithms, and check lists Companion site links to statistical databases that can be downloaded and used to perform the exercises from the book and practice statistical methods New in this Edition: New chapters on: multifactor tests on means of continuous data, equivalence testing, and advanced methods New topics include: trial randomization, treatment ethics in medical research, imputation of missing data, and making evidence-based medical decisions Updated database coverage and additional exercises Expanded coverage of numbers needed to treat and to benefit, and regression analysis including stepwise regression and Cox regression Thorough discussion on required sample size
