Applied Biostatistical Principles and Concepts Clinicians` Guide to Data Analysis and Interpretation, Laurens Holmes, Jr.

Автор: Cicchetti
Название: Methodological & Biostatistical
ISBN: 9026519648 ISBN-13(EAN): 9789026519642
Издательство: Taylor&Francis
Рейтинг:
Цена: 56130.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The goal of the second edition is to introduce the advance undergraduate or graduate student and more seasoned research scientists in any of the allied health sciences to a wide array of methodological and biostatistical issues.

Автор: Stephen C. Newman
Название: Biostatistical Methods in Epidemiology
ISBN: 0471369144 ISBN-13(EAN): 9780471369141
Издательство: Wiley
Рейтинг:
Цена: 178410.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An introduction to classical biostatistical methods in epidemiology, this text provides an introduction to a range of methods used to analyze epidemiologic data, with a focus on nonregression techniques. It also includes a discussion of measurement issues in epidemiology, especially confounding, with an overview of logistic and Cox regression.

Автор: Shigeyuki Matsui; John Crowley
Название: Frontiers of Biostatistical Methods and Applications in Clinical Oncology
ISBN: 9811001243 ISBN-13(EAN): 9789811001246
Издательство: Springer
Рейтинг:
Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Topics from modern cancer clinical trials include phase I clinical trials for combination therapies, exploratory phase II trials with multiple endpoints/treatments, and confirmative biomarker-based phase III trials with interim monitoring and adaptation.

Автор: Commenges
Название: Dynamical Biostatistical Models
ISBN: 1498729673 ISBN-13(EAN): 9781498729673
Издательство: Taylor&Francis
Рейтинг:
Цена: 117390.00 T
Наличие на складе: Невозможна поставка.
Описание:

Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software.

The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference.

Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.