The get-it-over-with-quickly approach to statistics has been encouraged - and often necessitated - by the short time allotted to it in most curriculums. If included at all, statistics is presented briefly, as a task to be endured mainly because pertinent questions may appear in subsequent examinations for licensure or other certifications. However, in later professional activities, clinicians and biomedical researchers will constantly be confronted with reports containing statistical expressions and analyses.
Not just a set of cookbook recipes, Principles of Medical Statistics is designed to get you thinking about data and statistical procedures. It covers many new statistical methods and approaches like box plots, stem and leaf plots, concepts of stability, the bootstrap, and the jackknife methods of resampling. The book is arranged in a logical sequence that advances from simple to more elaborate results. The text describes all the conventional statistical procedures, and offers reasonably rigorous accounts of many of their mathematical justifications. Although the conventional mathematical principles are given a respectful account, the book provides a distinctly clinical orientation with examples and teaching exercises drawn from real world medical phenomena.
Statistical procedures are an integral part of the basic background needed by biomedical researchers, students, and clinicians. Containing much more than most elementary texts, Principles of Medical Statistics fills the gap often found in the current curriculum. It repairs the imbalance that gives so little attention to the role of statistics as a prime component of basic biomedical education.
Table of Contents
Formation and Expression of Data
EVALUATING A SINGLE GROUP OF DATA
Central Index of a Group
Indexes of Inner Location
Inner Zones and Spreads
Probabilities and Standardized Indexes
Stability and Confidence Intervals: Means and Medians
Stability and Confidence Intervals: Binary Proportions
Communication and Display of Univariate Data
COMPARING TWO GROUPS OF DATA
Quantitative Contrasts: The Magnitude of Distinctions
Testing Stochastic Hypotheses
Permutation Procedures: Fisher Exact and Pitman-Welch Tests
Parametric Contrasts: Z and t Tests
Chi-Square Test and Evaluation of Two Proportions
Non-Parametric Rank Tests
Displays and Interpretations for Two-Group Contrasts
Special Arrangements for Rates and Proportions
Principles of Associations
Conformity and Marker Tests
Survival and Longitudinal Analyses
Alternative Hypotheses and Statistical 'Power'
Testing for 'Equivalence'
Multiple Stochastic Testing
Stratifications, Matchings, and Other "Adjustments."
Indexes of Categorical Association
Analysis of Variance
ANSWERS TO EXERCISES.