Biostatistics for Oncologists
Autor Kara-Lynne MD Leonard, Adam Sullivanen Limba Engleză Paperback – 9 apr 2018
Written for oncologists by oncologists, this practical text demystifies challenging statistical concepts and provides concise direction on how to interpret, analyze, and critique data in oncology publications, as well as how to apply statistical knowledge to understanding, designing, and analyzing clinical trials. With practical problem sets and twenty-five multiple choice practice questions with answers, the book is an indispensable review for anyone preparing for in-service exams, boards, MOC, or looking to hone a lifelong skill.
Key Features:
- Practically explains biostatistics concepts important for passing the hematology, medical oncology, and radiation oncology boards and MOC exams
- Provides guidance on how to read, understand, and critique data in oncology publications
- Gives relevant examples that are important for analyzing data in oncology, including the design and analysis of clinical trials
- Tests your comprehension of key biostatistical concepts with problem sets at the end of each section and a final section devoted to board-style multiple choice questions and answers
- Includes digital access to the eBook
Preț: 438.52 lei
Preț vechi: 461.60 lei
-5% Nou
Puncte Express: 658
Preț estimativ în valută:
83.91€ • 88.28$ • 69.66£
83.91€ • 88.28$ • 69.66£
Carte tipărită la comandă
Livrare economică 15-29 ianuarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780826168580
ISBN-10: 0826168582
Pagini: 202
Dimensiuni: 152 x 229 x 11 mm
Greutate: 0.34 kg
Editura: demosMEDICAL
ISBN-10: 0826168582
Pagini: 202
Dimensiuni: 152 x 229 x 11 mm
Greutate: 0.34 kg
Editura: demosMEDICAL
Descriere
Provides the essential biostatistical concepts, oncology-specific examples, and applicable problem sets for medical oncologists, radiation oncologists, and surgical oncologists. All examples are relevant to oncology and demonstrate how to apply core conceptual knowledge and methods related to hypothesis testing, correlation and regression, categorical data analysis and survival analysis.