7 results (0,13622 seconds)

Brand

Merchant

Price (EUR)

Reset filter

Products
From
Shops

Handbook of Item Response Theory Volume 1: Models

Systems Medicine Physiological Circuits and the Dynamics of Disease

Innovative Methods for Rare Disease Drug Development

Innovative Methods for Rare Disease Drug Development

In the United States a rare disease is defined by the Orphan Drug Act as a disorder or condition that affects fewer than 200 000 persons. For the approval of orphan drug products for rare diseases the traditional approach of power analysis for sample size calculation is not feasible because there are only limited number of subjects available for clinical trials. In this case innovative approaches are needed for providing substantial evidence meeting the same standards for statistical assurance as drugs used to treat common conditions. Innovative Methods for Rare Disease Drug Development focuses on biostatistical applications in terms of design and analysis in pharmaceutical research and development from both regulatory and scientific (statistical) perspectives. Key Features: Reviews critical issues (e. g. endpoint/margin selection sample size requirements and complex innovative design). Provides better understanding of statistical concepts and methods which may be used in regulatory review and approval. Clarifies controversial statistical issues in regulatory review and approval accurately and reliably. Makes recommendations to evaluate rare diseases regulatory submissions. Proposes innovative study designs and statistical methods for rare diseases drug development including n-of-1 trial design adaptive trial design and master protocols like platform trials. Provides insight regarding current regulatory guidance on rare diseases drug development like gene therapy.

GBP 44.99
1

Exposure-Response Modeling Methods and Practical Implementation

Exposure-Response Modeling Methods and Practical Implementation

Discover the Latest Statistical Approaches for Modeling Exposure-Response RelationshipsWritten by an applied statistician with extensive practical experience in drug development Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling from traditional pharmacokinetic-pharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book describes using measurement error models to treat sequential modeling fitting models with exposure and response driven by complex dynamics and survival analysis with dynamic exposure history. It also covers Bayesian analysis and model-based Bayesian decision analysis causal inference to eliminate confounding biases and exposure-response modeling with response-dependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation. Many examples illustrate the use of exposure-response modeling in experimental toxicology clinical pharmacology epidemiology and drug safety. Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios. Although application oriented this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition students can see how complicated methodology and models are applied to practical situations. | Exposure-Response Modeling Methods and Practical Implementation

GBP 44.99
1

Estimands Estimators and Sensitivity Analysis in Clinical Trials

Estimands Estimators and Sensitivity Analysis in Clinical Trials

The concepts of estimands analyses (estimators) and sensitivity are interrelated. Therefore great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language providing technical details real-world examples and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface such as between causal inference and clinical trialists as well as among clinicians statisticians and regulators when it comes to communicating decision-making objectives assumptions and interpretations of evidence. This book lays out a path toward bridging some of these gaps. It offers¿ A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges¿ A thorough treatment of intercurrent events (ICEs) i. e. postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)¿ Details on how estimands integrated into a principled study development process lay a foundation for coherent specification of trial design conduct and analysis needed to overcome the issues caused by ICEs:¿ A perspective on the role of the intention-to-treat principle¿ Examples and case studies from various areas¿ Example code in SAS and R¿ A connection with causal inference¿ Implications and methods for analysis of longitudinal trials with missing dataTogether the authors have offered the readers their ample expertise in clinical trial design and analysis from an industrial and academic perspective. | Estimands Estimators and Sensitivity Analysis in Clinical Trials

GBP 39.99
1

State-Space Methods for Time Series Analysis Theory Applications and Software

State-Space Methods for Time Series Analysis Theory Applications and Software

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover it can accommodate with a reasonable effort nonstandard situations such as observation errors aggregation constraints or missing in-sample values. Exploring the advantages of this approach State-Space Methods for Time Series Analysis: Theory Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web ResourceThe authors’ E4 MATLAB® toolbox offers all the computational procedures administrative and analytical functions and related materials for time series analysis. This flexible powerful and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work. | State-Space Methods for Time Series Analysis Theory Applications and Software

GBP 48.99
1

Games Gambling and Probability An Introduction to Mathematics

Games Gambling and Probability An Introduction to Mathematics

Many experiments have shown the human brain generally has very serious problems dealing with probability and chance. A greater understanding of probability can help develop the intuition necessary to approach risk with the ability to make more informed (and better) decisions. The first four chapters offer the standard content for an introductory probability course albeit presented in a much different way and order. The chapters afterward include some discussion of different games different ideas that relate to the law of large numbers and many more mathematical topics not typically seen in such a book. The use of games is meant to make the book (and course) feel like fun! Since many of the early games discussed are casino games the study of those games along with an understanding of the material in later chapters should remind you that gambling is a bad idea; you should think of placing bets in a casino as paying for entertainment. Winning can obviously be a fun reward but should not ever be expected. Changes for the Second Edition: New chapter on Game Theory New chapter on Sports Mathematics The chapter on Blackjack which was Chapter 4 in the first edition appears later in the book. Reorganization has been done to improve the flow of topics and learning. New sections on Arkham Horror Uno and Scrabble have been added. Even more exercises were added! The goal for this textbook is to complement the inquiry-based learning movement. In my mind concepts and ideas will stick with the reader more when they are motivated in an interesting way. Here we use questions about various games (not just casino games) to motivate the mathematics and I would say that the writing emphasizes a just-in-time mathematics approach. Topics are presented mathematically as questions about the games themselves are posed. Table of Contents Preface1. Mathematics and Probability 2. Roulette and Craps: Expected Value 3. Counting: Poker Hands 4. More Dice: Counting and Combinations and Statistics 5. Game Theory: Poker Bluffing and Other Games 6. Probability/Stochastic Matrices: Board Game Movement 7. Sports Mathematics: Probability Meets Athletics 8. Blackjack: Previous Methods Revisited 9. A Mix of Other Games 10. Betting Systems: Can You Beat the System? 11. Potpourri: Assorted Adventures in Probability Appendices Tables Answers and Selected Solutions Bibliography Biography Dr. David G. Taylor is a professor of mathematics and an associate dean for academic affairs at Roanoke College in southwest Virginia. He attended Lebanon Valley College for his B. S. in computer science and mathematics and went to the University of Virginia for his Ph. D. While his graduate school focus was on studying infinite dimensional Lie algebras he started studying the mathematics of various games in order to have a more undergraduate-friendly research agenda. Work done with two Roanoke College students Heather Cook and Jonathan Marino appears in this book! Currently he owns over 100 different board games and enjoys using probability in his decision-making while playing most of those games. In his spare time he enjoys reading cooking coding playing his board games and spending time with his six-year-old dog Lilly. | Games Gambling and Probability An Introduction to Mathematics

GBP 82.99
1