Master the essentials of data analysis in the medical field with our comprehensive selection of Biostatistics books at Jinnah Medical Bookshop—your leading online source for medical literature in Pakistan. Our collection includes the latest and most relevant textbooks designed for medical students, researchers, and professionals seeking to enhance their statistical skills. Enjoy the convenience of cash on delivery available across Pakistan, ensuring that the best resources arrive right at your doorstep. Equip yourself with the knowledge to analyze and interpret data effectively, and elevate your understanding of Biostatistics today!

 

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    Advanced Statistics in Regulatory Critical Clinical Initiatives is focused on the critical clinical initiatives introduced by the 21st Century Cure Act passed by the United States Congress in December 2016. The book covers everything from the outline of the initiatives to analysis on the effect on biopharmaceutical research and development. Advanced Statistics in Regulatory Critical Clinical Initiatives provides innovative ways to resolve common challenges in statistical research of rare diseases such small sample sizes and provides guidance for combined use of data. With analysis from regulatory and scientific perspectives this book is an ideal companion for researchers in biostatistics, pharmaceutical development, and policy makers in related fields.Key Features:
    • Provides better understanding of innovative design and analysis of each critical clinical initiatives which may be used in regulatory review/approval of drug development.
    • Makes recommendations to evaluate submissions accurately and reliably.
    • Proposes innovative study designs and statistical methods for oncology and/or rare disease drug development.
    • Provides insight regarding current regulatory guidance on drug development such as gene therapy and rare diseases.
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    Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome.Features
    • Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome
    • Uses consistent notation throughout the book for the different techniques presented
    • Explains in which situation each of these models should be used, and how they are linked to specific research questions
    • Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians
    • Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets
    This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.  Buy online Medical books at Gangaram Jinnah Medical Books Shop for home delivery and Cash on delivery to all over Pakistan. All kind of medical books are available.
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    APPLIED BIOSTATISTICS FOR THE HEALTH SCIENCESIn this newly revised edition of Applied Biostatistics for the Health Sciences, accomplished statistician Dr. Richard Rossi delivers a robust and easy-to-understand exploration of statistics in the context of applied health science and biostatistics. The book covers sample design, logistic regression, experimental design, survival analysis, basic statistical computation, and many more topics with a strong focus on the correct use and interpretation of statistics. The author also explains how to assess the quality of observed data, how to collect quality data, and the use of confidence intervals in conjunction with hypothesis and significance tests.
    • A thorough introduction to biostatistics, including explanations of fundamental concepts like populations, samples, statistics, biomedical studies, and data set examples
    • A comprehensive exploration of population descriptions, including qualitative and quantitative variables, multivariate data, measures of dispersion, and probability
    • Practical discussions of random sampling, summarizing random samples, and the measurement of the reliability of statistics
    • In-depth examinations of confidence intervals, statistical hypothesis testing, simple and multiple linear regression, and experimental design
    Perfect for health science and biostatistics students and professors at the upper undergraduate and graduate levels, Applied Biostatistics for the Health Sciences is also a must-read reference for practitioners and professionals in the fields of pharmacy, biochemistry, nursing, health care informatics, and the applied health sciences. 
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    Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners.Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more.The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research. 
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    Praise for Bayesian Thinking in Biostatistics:"This thoroughly modern Bayesian book …is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments…are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course…"-Thomas Louis, Johns Hopkins University"The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self- contained introduction to Bayesian inference for biomedical problems….Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems."- Peter Mueller, University of TexasWith a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently use, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests.Key Features
    • Applies a Bayesian perspective to applications in biomedical science
    • Highlights advances in clinical trial design
    • Goes beyond standard statistical models in the book by introducing Bayesian nonparametric methods and illustrating their uses in data analysis
    • Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation
    • Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own research
    The intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers, in generalAuthorsGary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.Purushottam (Prakash) W. Laud is Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin.Wesley O. Johnson is professor Emeritus in the Department of Statistics as the University of California, Irvine.  Buy online Medical books at Gangaram Jinnah Medical Books Shop for home delivery and Cash on delivery to all over Pakistan. All kind of medical books are available.
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    Break down biostatistics, make sense of complex concepts, and pass your classIf you're taking biostatistics, you may need or want a little extra assistance as you make your way through. Biostatistics For Dummies follows a typical biostatistics course at the college level, helping you understand even the most difficult concepts, so you can get the grade you need. Start at the beginning by learning how to read and understand mathematical equations and conduct clinical research. Then, use your knowledge to analyze and graph your data. This new edition includes more example problems with step-by-step walkthroughs on how to use statistical software to analyze large datasets. Biostatistics For Dummies is your go-to guide for making sense of it all.
    • Review basic statistics and decode mathematical equations
    • Learn how to analyze and graph data from clinical research studies
    • Look for relationships with correlation and regression
    • Use software to properly analyze large datasets
    Anyone studying in clinical science, public health, pharmaceutical sciences, chemistry, and epidemiology-related fields will want this book to get through that biostatistics course. 
     
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    Biostatistics Manual for Health Research: A Practical Guide to Data Analysis is a guide for researchers on how to apply biostatistics on different types of data. The book approaches biostatistics and its application from medical and health researcher’s point-of-view and has real and mostly published data for practice and understanding. The interpretation and meaning of the statistical results, reporting guidelines and mistakes are taught with real world examples. This is a valuable resource for biostaticians, students and researchers from medical and biomedical fields who need to learn how to apply statistical approaches to improve their research.
    • Applies a practical and solution centric approach to support readers to successfully manage their research data
    • Explains step-by-step the different biostatistical tests, including screenshots from the most common softwares used currently for easy consult
    • Summarizes the content of each chapter in concise text boxes to help readers find the right information when needed
     
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    This book constitutes revised selected papers from the 17th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2021, which was held virtually during November 15–17, 2021.The 19 papers included in these proceedings were carefully reviewed and selected from 26 submissions, and they focus on bioinformatics, computational biology, health informatics, cheminformatics, biotechnology, biostatistics, and biomedical imaging.
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    This book covers classic epidemiological designs that use a reference/control group, including case-control, case-cohort, nested case-control and variations of these designs, such as stratified and two-stage designs. It presents a unified view of these sampling designs as representations of an underlying cohort or target population of interest. This enables various extended designs to be introduced and analysed with a similar approach: extreme sampling on the outcome (extreme case-control design) or on the exposure (exposure-enriched, exposure-density, countermatched), designs that re-use prior controls and augmentation sampling designs. Further extensions exploit aggregate data for efficient cluster sampling, accommodate time-varying exposures and combine matched and unmatched controls. Self-controlled designs, including case-crossover, self-controlled case series and exposure-crossover, are also presented. The test-negative design for vaccine studies and the use of negative controls for bias assessment are introduced and discussed.This book is intended for graduate students in biostatistics, epidemiology and related disciplines, or for health researchers and data analysts interested in extending their knowledge of study design and data analysis skills.This book
    • Bridges the gap between epidemiology and the more mathematically oriented biostatistics books.
    • Assembles the wealth of epidemiological knowledge about observational study designs that is scattered over several decades of scientific publications.
    • Illustrates the performance of methods in real research applications.
    • Provides guidelines for implementation in standard software packages (Stata, R).
    • Includes numerous exercises, covering simple mathematical proofs, consideration of proposed or published designs, and practical data analysis.
     
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    The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change.Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations.Features
    • Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D
    • Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval
    • Offers a balanced approach to data science organization build
    • Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development
    • Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise
     
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    This book begins with an introduction of pragmatic cluster randomized trials (PCTs) and reviews various pragmatic issues that need to be addressed by statisticians at the design stage. It discusses the advantages and disadvantages of each type of PCT, and provides sample size formulas, sensitivity analyses, and examples for sample size calculation. The generalized estimating equation (GEE) method will be employed to derive sample size formulas for various types of outcomes from the exponential family, including continuous, binary, and count variables. Experimental designs that have been frequently employed in PCTs will be discussed, including cluster randomized designs, matched-pair cluster randomized design, stratified cluster randomized design, stepped-wedge cluster randomized design, longitudinal cluster randomized design, and crossover cluster randomized design. It demonstrates that the GEE approach is flexible to accommodate pragmatic issues such as hierarchical correlation structures, different missing data patterns, randomly varying cluster sizes, etc. It has been reported that the GEE approach leads to under-estimated variance with limited numbers of clusters. The remedy for this limitation is investigated for the design of PCTs. This book can assist practitioners in the design of PCTs by providing a description of the advantages and disadvantages of various PCTs and sample size formulas that address various pragmatic issues, facilitating the proper implementation of PCTs to improve health care. It can also serve as a textbook for biostatistics students at the graduate level to enhance their knowledge or skill in clinical trial design.Key Features:
    • Discuss the advantages and disadvantages of each type of PCTs, and provide sample size formulas, sensitivity analyses, and examples.
    • Address an unmet need for guidance books on sample size calculations for PCTs;
    • A wide variety of experimental designs adopted by PCTs are covered;
    • The sample size solutions can be readily implemented due to the accommodation of common pragmatic issues encountered in real-world practice;
    • Useful to both academic and industrial biostatisticians involved in clinical trial design;
    • Can be used as a textbook for graduate students majoring in statistics and biostatistics.
     
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    One of the hallmarks of the 21st century medicine is the emergence of digital therapeutics (DTx)—evidence-based, clinically validated digital technologies to prevent, diagnose, treat, and manage various diseases and medical conditions. DTx solutions have been gaining interest from patients, investors, healthcare providers, health authorities, and other stakeholders because of the potential of DTx to deliver equitable, massively scalable, personalized and transformative treatments for different unmet medical needs.Digital Therapeutics: Scientific, Statistical, Clinical, and Regulatory Aspects is an unparalleled summary of the current scientific, statistical, developmental, and regulatory aspects of DTx which is poised to become the fastest growing area of the biopharmaceutical and digital medicine product development. This edited volume intends to provide a systematic exposition to digital therapeutics through 19 peer-reviewed chapters written by subject matter experts in this emerging field.This edited volume is an invaluable resource for business leaders and researchers working in public health, healthcare, digital health, information technology, and biopharmaceutical industries. It will be also useful for regulatory scientists involved in the review of DTx products, and for faculty and students involved in an interdisciplinary research on digital health and digital medicine.Key Features:
    • Provides the taxonomy of the concepts and a navigation tool for the field of DTx.
    • Covers important strategic aspects of the DTx industry, thereby helping investors, developers, and regulators gain a better appreciation of the potential value of DTx.
    • Expounds on many existing and emerging state-of-the art scientific and technological tools, as well as data privacy, ethical and regulatory considerations for DTx product development.
    • Presents several case studies of successful development of some of the most remarkable DTx products.
    • Provides some perspectives and forward-looking statements on the future of digital medicine.
     
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    A disease is defined as rare if the prevalence is fewer than 200,000 in the United States. It is estimated that there are more than 7,000 rare diseases, which collectively affect 30 million Americans or 10% of the US population. This diverse and complex disease area poses challenges for patients, caregivers, regulators, drug developers, and other stakeholders. This book is proposedto give an overview of the common issues facing rare disease drug developers, summarize challenges specific to clinical development in small populations, discuss drug development strategies in the evolving regulatory environment, explain generation and utilization of different data and evidenceinside and beyond clinical trials, and use recent examples to demonstrate these challenges and the development strategies that respond to the challenges.Key Features:• Rare disease.• Drug development.• Innovative clinical trial design.• Regulatory approval.• Real-world evidence. 
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    Elements Of Clinical Study Design, Biostatistics & Research is designed as a toolbox for biomedical researchers. The book's primary focus is on applications in clinical research, and will benefit students and researchers involved in the biomedical field. This book addresses the problems that many practitioners experience in choosing and implementing fit-for-purpose data analysis methods to answer critical inferential questions for binomial and count data. The book is written in simple language avoiding complex derivations and mathematical formulae allowing it to explain the most basic concepts of research methodology. Making good use of numerous tables, graphs and tips, this book demystifies the process for readers.7 structured Chapters take the reader through the entire research process: choosing a question, designing a study, collecting the data, using univariate, bivariate, and multivariable analysis, and publishing the results. Each chapter discusses the strengths and weaknesses of the methods and suggests practical recommendations. An appendix on ethical research guidelines, along with a glossary and reference list comprehensively round up the book contents.It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of studies. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in the areas of biostatistics, epidemiology, medicine, pharmacy, and public health.
     
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    Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials provides a practical introduction to unconditional approaches to planning randomised clinical trials, particularly aimed at drug development in the pharmaceutical industry. This book is aimed at providing guidance to practitioners in using average power, assurance and related concepts. This book brings together recent research and sets them in a consistent framework and provides a fresh insight into how such methods can be used.Features:
    • A focuson normal theory linking average power, expected power, predictive power, assurance, conditional Bayesian power and Bayesian power.
    • Extensions of the concepts to binomial, and time-to-event outcomes and non-inferiority trials
    • An investigation into the upper bound on average power, assurance and Bayesian power based on the prior probability of a positive treatment effect
    • Application of assurance to a series of trials in a development program and an introduction of the assurance of an individual trial conditional on the positive outcome of an earlier trial in the program, or to the successful outcome of an interim analysis
    • Prior distribution of power and sample size
    • Extension of the basic approach to proof-of-concept trials with dual success criteria
    • Investigation of the connection betweenconditional and predictive power at an interim analysis and power and assurance
    • Introduction of the idea of surety in sample sizing of clinical trials based on the width of the confidence intervals for the treatment effect, and an unconditional version.
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    Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics.It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty.Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.
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    This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has become a relatively easy tasks for these huge datasets. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all these tasks, but also, has additional packages that were specifically designed for several statistical tasks related to biology and medical data. In addition, many scientific journals require the data analysis R scripts to ensure reproducibility of the submitted results. The first chapter of this book introduces many statistical concepts used in scientific research like study designs, sample, and population, and data types. Chapters 2, 4, and 5 cover the three main data types which are continuous data, categorical data, and time to event data. Chapter 3 discusses the popular continuous distribution that is the normal distribution along with its application to sample data. Chapter 6 is about the sampling distribution of different sample estimates along with a discussion of the famous central limit theorem (CLT). Chapters 7 and 8 are involved in confidence interval (CI) calculations, and Chapters 9–11 discuss several types of statistical tests like t-test, ANOVA, Chi-square, log-rank, etc. Finally, Chapters 12–14 deal with different regression types; linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression for time to event outcomes. In all these chapters, many examples from many scientific journal articles or built in data sets along with different codes and outputs are given to help your understanding of these numerous statistical concepts. I hope this book will be a great addition to your future biostatistical projects. Buy online Medical books at Gangaram Jinnah Medical Books Shop for home delivery and Cash on delivery to all over Pakistan. All kind of medical books are available.
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    Cancer is a dreaded disease. One in two people will be diagnosed with cancer within their lifetime. Medical Statistics for Cancer Studies shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It gives some background in cancer biology and genetics, followed by detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics. It includes lots of examples using real data from the author’s many years of experience working in a cancer clinical trials unit.

    Features:

    • A broad and accessible overview of statistical methods in cancer research
    • Necessary background in cancer biology and genetics
    • Details of statistical methodology with minimal algebra
    • Many examples using real data from cancer clinical trials
    • Appendix giving statistics revision.
     
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    Methodologies for Biosimilar Product Development covers the practical and challenging issues that are commonly encountered during the development, review, and approval of a proposed biosimilar product. These practical and challenging issues include, but are not limited to the mix-up use of interval hypotheses testing (i.e., the use of TOST) and confidence interval approach, a risk/benefit assessment for non-inferiority/similarity margin, PK/PD bridging studies with multiple references, the detection of possible reference product change over time, design and analysis of biosimilar switching studies, the assessment of sensitivity index for assessment of extrapolation across indications without collecting data from those indications not under study, and the feasibility and validation of non-medical switch post-approval.Key Features:
    • Reviews withdrawn draft guidance on analytical similarity assessment.
    • Evaluates various methods for analytical similarity evaluation based on FDA’s current guidelines.
    • Provides a general approach for the use of n-of-1 trial design for assessment of interchangeability.
    • Discusses the feasibility and validity of the non-medical switch studies.
    • Provides innovative thinking for detection of possible reference product change over time.
    This book embraces innovative thinking of design and analysis for biosimilar studies, which are required for review and approval of biosimilar regulatory submissions.  Buy online Medical books at Gangaram Jinnah Medical Books Shop for home delivery and Cash on delivery to all over Pakistan. All kind of medical books are available.
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    Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the development and success of subsequent phase II and III trials.The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol―making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development.Features
    • Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials
    • Describes the up-to-date theory and practice for model-assisted designs
    • Presents many practical challenges, issues, and solutions arising from early-phase clinical trials
    • Illustrates with many real trial applications
    • Offers numerous tips and guidance on designing dose finding and optimization trials
    • Provides step-by-step illustrations of using software to design trials
    • Develops a companion website (www.trialdesign.org) to provide freely available, easy-to-use software to assist learning and implementing model-assisted designs
    Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the design, conduct, and optimization of early-phase dose-finding trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart! 
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