Causal Inference in Pharmaceutical Statistics PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Causal Inference in Pharmaceutical Statistics PDF full book. Access full book title Causal Inference in Pharmaceutical Statistics by Yixin Fang. Download full books in PDF and EPUB format.
Author: Yixin Fang
Publisher: CRC Press
ISBN: 1040039715
Category : Mathematics
Languages : en
Pages : 412
Get Book Here
Book Description
Causal Inference in Pharmaceutical Statistics introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics. This book covers causal thinking for different types of commonly used study designs in the pharmaceutical industry, including but not limited to randomized controlled clinical trials, longitudinal studies, singlearm clinical trials with external controls, and real-world evidence studies. The book starts with the central questions in drug development and licensing, takes the reader through the basic concepts and methods via different study types and through different stages, and concludes with a roadmap to conduct causal inference in clinical studies. The book is intended for clinical statisticians and epidemiologists working in the pharmaceutical industry. It will also be useful to graduate students in statistics, biostatistics, and data science looking to pursue a career in the pharmaceutical industry. Key Features: Causal inference book for clinical statisticians in the pharmaceutical industry Introductory level on the most important concepts and methods Align with FDA and ICH guidance documents Across different stages of clinical studies: plan, design, conduct, analysis, and interpretation Cover a variety of commonly used study designs
Author: Yixin Fang
Publisher: CRC Press
ISBN: 1040039715
Category : Mathematics
Languages : en
Pages : 412
Get Book Here
Book Description
Causal Inference in Pharmaceutical Statistics introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics. This book covers causal thinking for different types of commonly used study designs in the pharmaceutical industry, including but not limited to randomized controlled clinical trials, longitudinal studies, singlearm clinical trials with external controls, and real-world evidence studies. The book starts with the central questions in drug development and licensing, takes the reader through the basic concepts and methods via different study types and through different stages, and concludes with a roadmap to conduct causal inference in clinical studies. The book is intended for clinical statisticians and epidemiologists working in the pharmaceutical industry. It will also be useful to graduate students in statistics, biostatistics, and data science looking to pursue a career in the pharmaceutical industry. Key Features: Causal inference book for clinical statisticians in the pharmaceutical industry Introductory level on the most important concepts and methods Align with FDA and ICH guidance documents Across different stages of clinical studies: plan, design, conduct, analysis, and interpretation Cover a variety of commonly used study designs
Author: Judea Pearl
Publisher: John Wiley & Sons
ISBN: 1119186862
Category : Mathematics
Languages : en
Pages : 162
Get Book Here
Book Description
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
Author: Agency for Health Care Research and Quality (U.S.)
Publisher: Government Printing Office
ISBN: 1587634236
Category : Medical
Languages : en
Pages : 236
Get Book Here
Book Description
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Author: Judea Pearl
Publisher: Cambridge University Press
ISBN: 052189560X
Category : Computers
Languages : en
Pages : 487
Get Book Here
Book Description
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...
Author: Yixin Fang (Statistician)
Publisher:
ISBN: 9781032560151
Category : Clinical trials
Languages : en
Pages : 0
Get Book Here
Book Description
"Causal Inference in Pharmaceutical Statistics introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics. This book covers causal thinking for different types of commonly used study designs in the pharmaceutical industry, including but not limited to randomized controlled clinical trials, longitudinal studies, single-arm clinical trials with external controls, and real-world evidence studies. The book starts with the central questions in drug development and licensing, takes the reader through the basic concepts and methods via different study types and through different stages, and conclude with a roadmap to conduct causal inference in clinical studies. The book is intended for clinical statisticians and epidemiologists working in the pharmaceutical industry. It will also be useful to graduate students in statistics, biostatistics, and data science looking to pursue a career in the pharmaceutical industry"--
Author: David J. Spiegelhalter
Publisher: John Wiley & Sons
ISBN: 9780471499756
Category : Mathematics
Languages : en
Pages : 416
Get Book Here
Book Description
READ ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author’s comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques. Illustrated throughout by detailed case studies and worked examples Includes exercises in all chapters Accessible to anyone with a basic knowledge of statistics Authors are at the forefront of research into Bayesian methods in medical research Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.
Author: James Penston
Publisher:
ISBN: 9781907313332
Category : Mathematics
Languages : en
Pages : 318
Get Book Here
Book Description
About Stats.con - How we've been fooled by statistics-based research in medicine: Statistics-based research is the method by which the causes of disease and the effectiveness of new treatments are investigated. Epidemiological studies and large-scale randomised controlled trials dominate medical research. Judged by the number of papers published each year, this type of research would appear to be a success. Yet it s a triumph of appearance over substance. We ve been cajoled into believing that great advances in medicine have occurred when, in fact, this isn t the case. Large RCTs are placed at the summit of the hierarchy of evidence and are claimed to be the most reliable means of establishing causal relationships in medical research. They are highly complex structures designed to identify small differences in outcome between the active treatment group and controls. But how do we know that the observed difference is caused by the drug? Proponents of RCTs assert that the method excludes alternative explanations namely, the unequal distribution of other causal factors, bias in the assessment of the outcome and chance. In other words, they believe that these studies have internal validity. The primary thesis of stats.con is that the grounds for causal inference in statistics-based research are lacking. Firstly, the components of the RCT including randomisation, allocation concealment, double-blind administration of treatment, the handling of withdrawals and drop-outs, and the statistical tests don t guarantee that the conditions for internal validity have been satisfied. Secondly, the frequentist approach to statistics, which continues to be used in almost all medical research studies despite being subjected to serious criticisms in recent years, is unsound. Thirdly, and most importantly, the inference from a small difference in outcome to the presence of a causal relationship is highly questionable. Given these arguments, it s of some importance to note that neither the results of individual RCTs nor the statistical method in general can be tested independently. This is an inevitable consequence of the subject matter of this type of research which involves heterogeneous samples with unknown mixtures of constituents. The inability to test the results of statistics-based research is of particular concern as fraud is more common than hitherto supposed in medical research. But even if we were to accept the validity of causal inference in this situation and to dismiss concerns about independent testing, we would still face the unpalatable truth that the product of statistics-based research is of little value. The reliability of any generalisation from the results of an individual study to the wider population of patients that is, the external validity is always open to question. We can never know whether the results of a RCT apply to either a particular patient or to a specified group. This is an enormous disadvantage in medicine. But that s not all. The size of the treatment effect in large-scale studies is very small. Indeed, it s so small that the true size of the effect is deliberately hidden by researchers and others with a vested interest in the outcome of the studies. When we look closely, the product of these studies is of dubious worth and doubtful meaning. The reasons for the widespread acceptance of statistics-based research are to be found in the events of the past fifty years or more. History shows how the advocates have used every means at their disposal to spread a flawed methodology and how their views have infiltrated the thinking of generations of researchers, practicing physicians and others involved in the care of patients. But this doesn t apply only to medical research. Many other academic disciplines use similar methods. If, as is argued in stats.con, the case against statistics-based research is made, then the implications extend far beyond the field of medicine.
Author: Deborah G. Mayo
Publisher: Cambridge University Press
ISBN: 1108563309
Category : Mathematics
Languages : en
Pages : 503
Get Book Here
Book Description
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Author: Adam LaCaze
Publisher: Springer Nature
ISBN: 3030291790
Category : Medical
Languages : en
Pages : 475
Get Book Here
Book Description
This volume covers a wide range of topics concerning methodological, epistemological, and regulatory-ethical issues around pharmacology. The book focuses in particular on the diverse sources of uncertainty, the different kinds of uncertainty that there are, and the diverse ways in which these uncertainties are (or could be) addressed. Compared with the more basic sciences, such as chemistry or biology, pharmacology works across diverse observable levels of reality: although the first step in the causal chain leading to the therapeutic outcome takes place at the biochemical level, the end-effect is a clinically observable result—which is influenced not only by biological actions, but also psychological and social phenomena. Issues of causality and evidence must be treated with these specific aspects in mind. In covering these issues, the book opens up a common domain of investigation which intersects the deeply intertwined dimensions of pharmacological research, pharmaceutical regulation and the related economic environment. The book is a collective endeavour with in-depth contributions from experts in pharmacology, philosophy of medicine, statistics, scientific methodology, formal and social epistemology, working in constant dialogue across disciplinary boundaries.
Author: Frank E. Harrell
Publisher: Springer Science & Business Media
ISBN: 147573462X
Category : Mathematics
Languages : en
Pages : 583
Get Book Here
Book Description
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
