* GS. Nguyễn Văn Tuấn

Các bài về Thống kê lâm sàng của GS Nguyễn Văn Tuấn

Thiết kế nghiên cứu và Thống Kê Y học.

TS Nguyễn Ngọc Rạng. Bệnh Viện An Giang.

Nhà xuất bản y học, 2012

Statistics.vn của một nhóm gồm các bác sĩ, kĩ sư, giảng viên, chuyên gia trong và ngoài nước. Mục tiêu của statistics.vn là giới thiệu và chia sẻ những kiến thức, kinh nghiệm trong giảng dạy và ứng dụng khoa học thống kê trong nghiên cứu khoa học.

* Betty Kirwood
London School of Hygiene and Tropical Medicine.
Dịch giả: Đỗ Văn Dũng
Can ban thong ke y hoc.pdf
Adobe Acrobat Document 1.4 MB


StatSoft, Inc. (2010). Electronic Statistics Textbook. Tulsa, OK: StatSoft.

An Introduction to Statistical Methods and Data Analysis


Authors: R. Lyman Ott and Micheal T. Longnecker


Publisher: Duxbury Press 2008; 1296 pages


Basic Statistics and Epidemiology: A Practice Guide


Author: Antony Stewart


Publisher: Radcliffe Publishing Ltd, 2nd edition (2002); 144 pages

Basic statistics:  A primer for the Biomedical Ssciences. 4th. 


Authors: Dunn O. Jean and Virginia A. clark, 2009. 


Publisher: Wiley; 4 edition (July 27, 2009); 272 pages

BMC Books 2002.


This is one of the bestselling introductions to medical statistics of all time. The book summarizes in a friendly manner basic statistical concepts and points out many frequently made statistical errors. This edition has been revised throughout, especially in the areas of binary data to deal with relative risk., absolute risk and the evidence-based criteria of numbers need to treat. Each chapter now has a section on reading and reporting statistics, and self testing at the end of each section makes this an ideal learning tool.

BMC Books 2004.

Updated companion volume to the ever popular Statistics at Square One (SS1) Statistics at Square Two, Second Edition, helps you evaluate the many statistical methods in current use. Going beyond the basics of SS1, it covers sophisticated methods and highlights misunderstandings. Easy to read, it includes annotated computer outputs and keeps formulas to a minimum. Worked examples of methods such as multiple and logical regression reinforce the text. Each chapter concludes with exercises to stimulate learning. All those who need to understand statistics in clinical research papers and apply them in their own research will value this compact and coherent guide.

The easy, practical, up-to-date introduction to statistics–for everyone! Thought you couldn’t learn statistics? You can–and you will! One easy step at a time, this fully updated book teaches you all the statistical techniques you’ll need for finance, quality, marketing, the social sciences, or anything else! Simple jargon-free explanations help you understand every technique. Practical examples and worked-out problems give you hands-on practice. Special sections present detailed instructions for developing statistical answers, using spreadsheet programs or any TI-83/TI-84 compatible calculator. This edition delivers new examples, more detailed problems and sample solutions, plus an all-new chapter on powerful multiple regression techniques. Hate math? No sweat. You’ll be amazed at how little you need. Like math? Optional “Equation Blackboard” sections reveal the mathematical foundations of statistics right before your eyes!

This book applies power analysis to both null hypothesis and minimum-effect testing using the same basic model. Through the use of a few relatively simple procedures and examples from the behavioral and social sciences, the authors show readers with little expertise in statistical analysis how to quickly obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to understand problems of study design, to evaluate research, and to choose the appropriate criterion for defining "statistically significant" outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis that is based on the F statistic.

Basic Biostatistics presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. Methods range from the elementary, including descriptive statistics, study design, statistical interference, categorical variables, evaluation of diagnostic tests, comparison of means, linear regression, and logistic regression. These introductory methods create a portfolio of biostatistical techniques for both novice and expert researchers. More complicated statistical methods are introduced as well, including those requiring either collaboration with a biostatistician or the use of a statistical package. Specific topics of interest include microarray analysis, missing data techniques, power and sample size, statistical methods in genetics. Expert advice is given on when to seek statistical help, and how to conduct a meeting with the statistical collaborator or consultant. Basic Biostatistics is an essential resource for researchers at every level of their career.

This book compiles 30 carefully prepared and peer-reviewed articles form eminent researchers in the modern-day biomedical sciences. With sections on Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics, it provides more specific details on concepts, methods, and algorithms.

Provides a wide view of contemporary methodological developments and technical innovations with a focus on applications. A related website offers datasets used or referred to in the book and suggests links to the commercial or freely downloadable software packages referred to in the various chapters. 


This book presents new and powerful advanced statistical methods that have been used in modern medicine, drug development, and epidemiology. Some of  these methods were initially developed for tackling medical problems. All 29 chapters are self-contained. Each chapter represents the new development and future research topics for a medical or statistical branch. For the benefit of readers with different statistical background, each chapter follows a similar style: the explanation of medical challenges, statistical ideas and strategies, statistical methods and techniques, mathematical remarks and background and reference. All chapters are written by experts of the respective topics. 

This text describes current statistical tools that are used to analyze the association between possible risk factors and the actual risk of disease. Beginning with a broad conceptual framework on the disease process, it describes commonly used techniques for analyzing proportions and disease rates. These are then extended to model fitting, and the common threads of logic that bind the two analytic strategies together are revealed. Each chapter provides a descriptive rationale for the method, a worked example using data from a published study, and an exercise that allows the reader to practice the technique. Each chapter also includes an appendix that provides further details on the theoretical underpinnings of the method. Among the topics covered are Mantel-Haenszel methods, rates, survival analysis, logistic regression, and generalized linear models. Methods for incorporating aspects of study design, such as matching, into the analysis are discussed, and guidance is given for determining the power or the sample size requirements of a study. This text will give readers a foundation in applied statistics and the concepts of model fitting to develop skills in the analysis of epidemiological data.

This book fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, the book requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.