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E-raamat: Introduction to Statistical Methods for Clinical Trials

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Edited by (University of Wisconsin-Madison, USA), Edited by (University of Wisconsin, Madison, USA)
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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.

Arvustused

There is much good material in this book. The individual chapters are well written and cover the technical aspects as well. A major strength is the ordering of topics to follow the thought process used in the development and implementation of a protocol from defining the question to reporting results. There are careful discussions on fundamental principles and the pivotal role played by statistics is well brought out. there is much that practicing pharmaceutical statisticians will find useful in this book. They will find the coverage of fundamental principles useful and the technical content of the book a good reference source. Pharmaceutical Statistics, 2010

fits the need for a contemporary text and handbook that is oriented toward the clinical trial statistician. I highly recommend it and look forward to using it as both a primary and supplemental text in our curriculum, as well as a research resource. James J. Dignam, University of Chicago, JASA, March 2009

The (technical) statistical content is the main focus of the book and this is what helps it to stand apart from most others on clinical trials (even the more obviously statistically orientated ones). It takes the reader to quite a technical background that would serve him or her well if moving on to research problems in the various areas covered, yet does not lose sight of practical issues. For those of us with the interest (and need) to grapple with these more statistical issues, I wholeheartedly recommend it. Biometrics, December 2008

The book is very well written and clear. the authors generally strike the right balance for the intended audience. The inclusion of many historically important as well as contemporary examples to illustrate various points throughout the text is a major strength, as is the inclusion of several modern topics not seen in other texts. As a basis for a course in clinical trials for graduate students in biostatistics, this book is outstanding. In addition, statisticians in the pharmaceutical industry, government, or academia will find this text extremely informative and useful. Michael P. McDermott, University of Rochester Medical Center, Journal of Biopharmaceutical Statistics, 2008

List of figures
xi
List of tables
xv
Preface xix
Author Attribution xxiii
Introduction to Clinical Trials
1(28)
History and Background
3(2)
Ethics of Clinical Research
5(4)
Types of Research Design and Types of Trials
9(6)
The Need for Clinical Trials
15(3)
The Randomization Principle
18(1)
Timing of a Clinical Trial
18(2)
Trial Organization
20(2)
Protocol and Manual of Operations
22(1)
Regulatory Issues
22(4)
Overview of the Book
26(3)
Defining the Question
29(46)
Statistical Framework
31(9)
Elements of Study Question
40(4)
Outcome or Response Measures
44(12)
The Surrogate Outcome
56(8)
Composite Outcomes
64(9)
Summary
73(1)
Problems
73(2)
Study Design
75(40)
Early Phase Trials
76(9)
Phase III/IV Trials
85(16)
Non-inferiority Designs
101(5)
Screening, Prevention, and Therapeutic Designs
106(3)
Adaptive Designs
109(3)
Conclusions
112(1)
Problems
112(3)
Sample Size
115(26)
Sample Size versus Information
116(2)
A General Setup for Frequentist Designs
118(4)
Loss to Follow-up and Non-adherence
122(2)
Survival Data
124(10)
Clustered Data
134(2)
Tests for Interaction
136(1)
Equivalence/Non-inferiority Trials
137(1)
Other Considerations
138(1)
Problems
139(2)
Randomization
141(30)
The Role of Randomization
141(7)
Fixed Randomization Procedures
148(7)
Treatment- and Response-Adaptive Randomization Procedures
155(6)
Covariate-Adaptive Randomization Procedures
161(4)
Summary and Recommendations
165(3)
Problems
168(3)
Data Collection and Quality Control
171(30)
Planning for Collection of Clinical Trial Data
172(13)
Categories of Clinical Data
185(9)
Data Quality Control
194(5)
Conclusions
199(2)
Survival Analysis
201(30)
Background
201(2)
Estimation of Survival Distributions
203(10)
Comparison of Survival Distributions
213(6)
Regression Models
219(8)
Composite Outcomes
227(1)
Summary
228(1)
Problems
229(2)
Longitudinal Data
231(36)
A Clinical Longitudinal Data Example
232(2)
The Subject-specific Model
234(3)
Two-stage Estimation
237(5)
The Random-effects, Subject-specific Model
242(4)
The Population-average (Marginal) Model
246(6)
Restricted Maximum Likelihood Estimation (REML)
252(1)
Standard Errors
253(2)
Testing
255(3)
Additional Levels of Clustering
258(2)
Generalized Estimating Equations for Non-normal Data
260(3)
Missing Data
263(1)
Summary
264(3)
Quality of Life
267(22)
Defining QoL
268(1)
Types of QoL Assessments
268(3)
Selecting a QoL Instrument
271(2)
Developing a QoL Instrument
273(1)
Quality of Life Data
273(2)
Analysis of QoL Data
275(11)
Summary
286(3)
Data Monitoring and Interim Analysis
289(50)
Data and Safety Monitoring
290(2)
Examples
292(1)
The Repeated Testing Problem
293(6)
Group Sequential Tests
299(12)
Triangular Test
311(4)
Curtailment Procedures
315(7)
Inference Following Sequential Tests
322(7)
Discussion
329(7)
Problems
336(3)
Selected Issues in the Analysis
339(38)
Bias in the Analysis of Clinical Trial Data
339(1)
Choice of Analysis Population
340(14)
Missing Data
354(10)
Subgroup Analyses
364(6)
Multiple Testing Procedures
370(5)
Summary
375(1)
Problems
376(1)
Closeout and Reporting
377(16)
Closing Out a Trial
377(1)
Reporting Trial Results
378(14)
Problems
392(1)
Delta Method, Maximum Likelihood Theory, and Information
393(12)
Delta Method
393(1)
Asymptotic Theory for Likelihood Based Inference
393(2)
Hypothesis Testing
395(4)
Computing the MLE
399(1)
Information
400(3)
Brownian Motion
403(2)
References 405(22)
Index 427


Thomas D. Cook, David L. DeMets
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