Medical Statistics: A Practical Approach

Book Description

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: Correlation and Regression

Includes the concept of regression to the mean, generalized linear regression models (Poisson and Logistic), and discrimination of new data to belong to which sample data sets.

Chapter 8: Nonparametric Inference

Covers the Kolmogorov and Smirnov test.

Chapter 9: Sample Size Estimation

Includes estimation and hypothesis testing to determine sample sizes.

Chapter 10: Study Design

Discusses the design of studies 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 this chapter.

Chapter 11: Inference on Contingency Tables

Covers in detail the analysis of contingency tables, including 2 x 2, two-way, and three-way tables. 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, Bartlett's on second-order interaction) and Simpson's paradox are covered in Section 11.3.

Chapter 12: Survival Data Analysis

Introduces two methods (life-table and Kaplan-Meier) for estimating the survivor function in Section 12.2. Covers four methods (maximum likelihood, Armitage's preference, Wald's sequential sign, and Armitage's restricted sequential) for comparing two survival curves in Section 12.3. Discusses the proportional hazard model and the log-rank test in Sections 12.4 and 12.5, respectively. Additionally, advanced techniques such as Armitage's preference method, Armitage's restricted sequential test, and Wald's sequential sign test are included for comparing survival curves. The book also treats inference on contingency tables in more detail than other books.