MARC details
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03967nam a22003017a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
CUTN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20231204121340.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
231204b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781032242453 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781138368835 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780429428371 |
| 041 ## - LANGUAGE CODE |
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| Language |
English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
23 |
| Classification number |
610.724 |
| Item number |
RAB |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Rabbee, Nusrat. |
| 240 ## - UNIFORM TITLE |
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| Uniform title |
<a href="Biomarker Analysis in Clinical Trials with R">Biomarker Analysis in Clinical Trials with R</a> |
| 245 ## - TITLE STATEMENT |
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| Title |
Biomarker Analysis in Clinical Trials with R / |
| Statement of responsibility, etc |
Nusrat Rabbee. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Florida : |
| Name of publisher, distributor, etc |
CRC Press, |
| Date of publication, distribution, etc |
2021. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxiii, 204 p. : |
| Other physical details |
ill. ; |
| Dimensions |
22 cm. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Contents |
Section I Pharmacodynamic Biomarkers<br/><br/>1. Introduction<br/><br/>2. Toxicology Studies<br/><br/>3. Bioequivalence Studies<br/><br/>4. Cross-Sectional Profile of Pharmacodynamics Biomarkers<br/><br/>5. Timecourse Profile of Pharmacodynamics Biomarkers<br/><br/>6. Evaluating Multiple Biomarkers<br/><br/>Section II Predictive Biomarkers<br/><br/>7. Introduction<br/><br/>8. Operational Characteristics of Proof-of-Concept Trials<br/><br/>with Biomarker-Positive and -Negative Subgroups<br/><br/>9. A Framework for Testing Biomarker Subgroups in<br/><br/>Confirmatory Trials<br/><br/>10. Cutoff Determination of Continuous Predictive<br/><br/>Biomarker for a Biomarker–Treatment Interaction<br/><br/>11. Cutoff Determination of Continuous Predictive Biomarker<br/><br/>Using Group Sequential Methodology<br/><br/>12. Adaptive Threshold Design<br/><br/>13. Adaptive Seamless Design (ASD)<br/><br/>Section III Surrogate Endpoints<br/><br/>14. Introduction<br/><br/>15. Requirement # 1: Trial Level – Correlation Between<br/><br/>Hazard Ratios in Progression-Free Survival and Overall<br/><br/>Survival Across Trials<br/><br/>16. Requirement # 2: Individual Level – Assess the Correlation<br/><br/>Between the Surrogate and True Endpoints After Adjusting<br/><br/>for Treatment (R2<br/><br/>indiv)<br/><br/>17. Examining the Proportion of Treatment Effect in AIDS Clinical<br/><br/>Trials<br/><br/>18. Concluding Remarks<br/><br/>Section IV Combining Multiple Biomarkers<br/><br/>19. Introduction<br/><br/>20. Regression-Based Models<br/><br/>21. Tree-Based Models<br/><br/>22. Cluster Analysis<br/><br/>23. Graphical Models<br/><br/>Section V Biomarker Statistical Analysis Plan |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry, much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating, understanding and synthesizing biomarker data. -From the Foreword, Jared Christensen, Vice President, Biostatistics Early Clinical Development, Pfizer, Inc.<br/><br/>Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R, the book helps students, researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers.<br/><br/>Features:<br/><br/>Analysis of pharmacodynamic biomarkers for lending evidence target modulation.<br/>Design and analysis of trials with a predictive biomarker.<br/>Framework for analyzing surrogate biomarkers.<br/>Methods for combining multiple biomarkers to predict treatment response.<br/>Offers a biomarker statistical analysis plan.<br/>R code, data and models are given for each part: including regression models for survival and longitudinal data, as well as statistical learning models, such as graphical models and penalized regression models. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Predictive Biomarker |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
pharmacodynamic |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
biostatisticians |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Toxicology |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
biomarkers |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Text Books |
