This book is suitable to be used as a textbook for all levels of students in medical school. It is also useful as a reference book for students interested in the application of biostatistics in medicine. Materials from the Introduction to Chapter 6 are similar to those of an elementary statistical textbook.This book is more modern than the current textbook in medical statistics. In this book, biostatistics and epidemiologic concepts are nicely blended. In contrast to the fallacy of the p-value, it introduces the Bayes factor as a measure of the evidence hidden in the sample data. It illustrates the application of the regression to the mean in medicine. Many epidemiologic concepts such as sensitivity and specificity of the diagnostic test, classification and discrimination, types of bias, etc. are discussed in the book.Chapter 7 on 'Correlation and Regression' includes the concept of regression to the mean, generalized linear (Poisson and Logistic) regression models, and discrimination of new data to belong to which sample data sets. Chapter 8 covers the nonparametric inference, including Kolmogorov and Smirnov test. Via the estimation and hypothesis testing, sample sizes are determined in Chapter 9. Chapter 10 discusses the study of design for collecting sample data, including cohort, cross-sectional, case-control, and clinical trial. In addition, types of bias are expounded as a last section in Chapter 10.Chapter 11 covers in detail the inference on contingency tables, including 2 x 2, two-way, and three-way. Five tests (Pearson, log-odds-ratio, Fisher-Irwin, McNemar, and Ejigou-McHugh) are listed in Section 11.1. Six tests (Pearson, First-order interaction, Yate's linear trend, Stuart's marginal homogeneity, Kendall, and Wilcoxon-Mann-Whitney) are described in Section 11.2. Three tests (Pearson, log-odds-ratio on first-order interaction, Barlett's on second-order interaction) and Simpson's paradox are covered in Section 11.3.Chapter 12 covers analysis of survival data. Two methods (life-table and Kaplan-Meier) are introduced for estimating the survivor function in Section 12.2. Four methods (maximum likelihood, Armitage's preference, Wald's sequential sign, and Armitage's restricted sequential) for comparing two survival curves are covered in Section 12.3. Proportional hazard model and the log-rank test are discussed, respectively, in Section 12.4 and 12.5.In addition, advanced techniques in comparing two survival curves are included in the book such as Armitage's preference method, Armitage's restricted sequential test and Wald's sequential sign test. Also, inference on contingency tables are treated in more detail than other books.
Pearson's. correlation. Details of the method • It is used to estimate the strength of linear relationship between two continuous variables • It gives a correlation coefficient – often denoted by 'r' The yi and calculations their means ...
For the keen student who does not want a book for mathematicians, this is an excellent first book on medical statistics." Essential Medical Statistics is a classic amongst medical statisticians.
An Introduction to Medical Statistics
This new edition of Medical Statistics at a Glance: Presents key facts accompanied by clear and informative tables and diagrams Focuses on illustrative examples which show statistics in action, with an emphasis on the interpretation of ...
A concise, straightforward introduction to medical statistics, this book covers all the topics which a medical student or research worker is likely to encounter in routine work.
... 134, 135, 138 software, 142–144 survival parameterization, 130 nested case–control approach, 6 network algorithms, 52 Newton–Raphson (NR) algorithm, 154 Neyman–Pearson framework, 316, 317 non-informative, 310, 315, 318, 319, 321, ...
Short, readable, and useful, this book provides the essential, basic information without becoming bogged down in the
Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible.
New in this edition: Measuring survival: Kaplan-Meier survival curves; comparingsurvival in two or more groups.Hazard ratios and the Cox regressionmodel. Systematic review: methods and problems; combining results -meta-analytic methods.
Using real data and including dozens of interesting data sets, this bestselling text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.