Understanding Clinical Data Analysis
Learning Statistical Principles from Published Clinical Research

Authors: Ton J. Cleophas, Aeilko H. Zwinderman



The book uses the best-help-there-is for making the difficult issues understandable by using real data examples rather than hypothetical examples

Complementarily to real data examples, the book continually gives a philosophical treatise of the basics of the scientific method.

The book explains all of the novel issues of clinical data analysis from the past few years.

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.

In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.

Table of contents

Front Matter

Randomness

Randomized and Observational Research

Randomized Clinical Trials, History, Designs

Randomized Clinical Trials, Analysis Sets, Statistical Analysis, Reporting Issues

Discrete Data Analysis, Failure Time Data Analysis

Quantitative Data Analysis

Subgroup Analysis

Interim Analysis

Multiplicity Analysis

Medical Statistics: A Discipline at the Interface of Biology and Mathematics

Back Matter