A fully updated edition of this key text on mixed models, focusing on applications in medical researchThe application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed mo...

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex model...

A comprehensive compilation of new developments in data linkage methodologyThe increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. R...

This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians. Additionally it aims to stimulate academic interest in the field of Nonclinical Statistics and promote this as an important discipline in its own right. This text brings together for the first time in a single volume a comprehensive survey of methods important to the nonclinical science areas within the pharmaceutical and biotechnology industries. ...

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent...

Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On ...

Crucial decisions concerning surgical treatment are often based on the results of various methods used for assessing colorectal function. Yet the new methods which have resulted from recent technological advances are still often regarded as research tools or viewed with skepticism by the practicing physician. New horizons are opened up for the coloproctologist in this book which fully documents the scope of established and recently developed investigative techniques. These include manometr...

In the era of Evidence Based Medicine, health professionals are required to fully understand design, analysis and interpretation of the results of research. Furthermore, they should be able to assess the needs of their communities and respond accordingly. To achieve these goals, clinicians need to be familiar with the basic concepts of epidemiology and biostatistics. But epidemiology is more than “the study of.” Its application and practice are essential to address public health issues. Th...

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and mana...

A comprehensive guidebook to the current methodologies and practices used in health surveysA unique and self-contained resource, Handbook of Health Survey Methods presents techniques necessary for confronting challenges that are specific to health survey research. The handbook guides readers through the development of sample designs, data collection procedures, and analytic methods for studies aimed at gathering health information on general and targeted p...

This volume contains rigorously reviewed papers on the topics presented by students at The 9th Annual University of North Carolina at Greensboro Regional Mathematics and Statistics Conference (UNCG RMSC) that took place on November 2, 2013. All papers are coauthored by student researchers and their faculty mentors. This conference series was inaugurated in 2005, and it now attracts over 150 participants from over 30 universities from North Carolina and surrounding states. The conf...

Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking. The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and stat...