Statistics for Epidemiology Review

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(More customer reviews)I had a chance to read this book cover to cover. All I can say is "absolutely outstanding", short of calling it a historical masterpiece in the field. Very rarely do I encounter an epidemiology or biostatistic textbook that reads so well. It is optimally reader friendly; the author appears to have such a talent in explaining some most sophisticated epidemiological and statistical concepts in such a simplified language. Yet he does not sacrifice the inclusion of some very advanced epidemiological and statistical concepts. New concepts such as causal graphs and instrumental variables are also included and explained beautifully. I strongly recommend this book to all early to intermediate graduate students majoring in Epidemiology. Established epidemiologists may wish to read this book to refresh and update their knowledge. I hope the author writes more textbooks with the same style.

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Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches.Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies.

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Quality of Life: The Assessment, Analysis and Interpretation of Patient-reported Outcomes Review

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(More customer reviews)Excellent review of QOL issues in health care. Provides provoking thoughts regarding measurement of patient outcomes. A must for those performing QOL research.

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Methods for Meta-Analysis in Medical Research (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) Review

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(More customer reviews)This book is a survey of meta-analysis as applied to medical research. It covers fixed and random effects models, heterogeneity, publication bias, study quality and sensitivity analysis, and a number of more advanced topics, including Bayesian methods, problems with missing data, meta-analysis of other kinds of data, such as survival or observational data, and cumulative meta-analysis. The focus is on the statistical aspects of meta-analysis, not on how to conduct a literature search or code the sources, which are well documented elsewhere. The early part of the book is tutorial, and provides formulas for computing pooled effect sizes, and displays sample data sets in tables, along with corresponding graphical output. The latter part of the book discusses more advanced topics, but because of their inherent complexity, does not give complete details to allow reproducing the results. The mathematics is of moderate complexity. If you don't know about odds ratios, multivariate regression, and if notation such as y ~ N(0,1) leaves you puzzled, then this book is probably not for you. It makes only extremely brief use of linear algebra. It would be appropriate for quantitatively-inclined clinicians, or those learning about biostatistics and health services research. Although multi-authored, it has been edited for uniform style. It is quite readable, although there are a few annoying typos.

Negatives: the book was published in 2000, so it is not up-to-date, especially with respect to the capabilities of currently available software packages. The book's website no longer exists, so the claim that software code used in the examples is available to readers is a false one. This is a significant problem if you want to reproduce some of the Bayesian calculations, for example. Given these limitations, I feel it's not worth the $100+ price. A good book, but borrow it from the library.

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With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:* Provides a thorough criticism and an up-to-date survey of meta-analysis methods* Emphasises the practical approach, and illustrates the methods by numerous examples* Describes the use of Bayesian methods in meta-analysis* Includes discussion of appropriate software for each analysis* Includes numerous references to more advanced treatment of specialist topics* Refers to software code used in the examples available on the authors' Web sitePractising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

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The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series) Review

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(More customer reviews)With the increase in cancer trials both in medical research and the pharmaceutical industry, medical diagnostic tests including medical imaging evaluations are being used more and more. There is a need for adjudication when radiologists disagree on a diagnosis and some standard statistical measures from other fields are finding new application.
This book provides a thorough background on the subject, the methodology and the applications. It is very clearly written and not overly technical. The methodology is the classical frequentist approach to statistical inference. Recently, Lyle Broemeling has published a book on this topic that takes exclusively the Bayesian approach and explains the Bayesian approach for those who are not acquainted with it. When dealing with predictions based on evolving data the Bayesian approach might be more natural. This would be the case in a clinical trial where the data is reviewed sequentially.
A particularly important part of diagnostic accuracy analysis is the area under the ROC curve. Pepe does an excellent job of covering that topic. This text is referenced often in Broemeling's book since it provides an excellent explanation of the same topics using the frequentist approach that he covers with the Bayesian approach.

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This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.

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Interpreting the Medical Literature Review

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(More customer reviews)A statistician might find Gehlbach's explanations a bit too simplistic, but I think it gives a fantastic overview of how to approach interpretation of journal articles. An attending asked us to buy the second edition of this book when I was a 3rd Year med student, and I still use it today teaching residents. He does an excellent job using examples from the literature to illustrate his points. And, unlike other statistical texts, the index can help you grasp topics like the null hypothesis or type I error very quickly.

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This text aims to help students and clinicians to understand the medical literature by instilling the essential skills necessary to evaluate article findings. The principles of study design, data analysis, statistical significance and data interpretation are all examined.--This text refers to an out of print or unavailable edition of this title.

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Primer of Applied Regression & Analysis of Variance Review

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(More customer reviews)Like all advanced stats books, this one has mathematical rigor and plenty of examples. But unlike the others, this one is written from the point of view of a biologist. You won't just learn the math, you'll learn how to make sense of the results. The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book to learn about various kinds of ANOVAS and regressions.

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Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA). (20010101)

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Medical Biostatistics (Chapman & Hall/CRC Biostatistics Series) Review

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(More customer reviews)I have long been looking for a book that makes biostatistics simple and readable to medical professionals. This book on Medical Biostatistics by Indrayan and Sarmukaddam (Marcel Dekker) is a very sincere attempt in making the subject of biostatistics look like a medical discipline. This is one of the objectives stated in the book, and seems to have been very adequately achieved. The language used is friendly to the medical fraternity. A large number of statistical methods have been discussed that are commonly used in medicine and health. Mathematics is minimal and explantions appeal to the common sense. Statistical fallacies are also discussed. Strongly recommended for all professionals and students of medical related disciplines who collect, disseminate or use data in any form.

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Modeling in Medical Decision Making: A Bayesian Approach (Statistics in Practice) Review

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(More customer reviews)This is a nice readable book about decision theory from the Bayesian perspective. In addition to providing the foundations of Bayesian decision theory including utility theory the author prvides many real world research examples including meta-analyses.
Unlike his more recent and highly statistically sophisticated text on Bayesian decision theory this concentrates on medical examples and his written at a level to appeal to clinicians as well as statisticians. Decision trees and methods toevaluate quality of life years are introduced and used in the examples.

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The Biostatistics Cookbook: The Most User-Friendly Guide for the Bio/Medical Scientist Review

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(More customer reviews)If you want a straightforward introduction to statistics with enough theory included if you want to dig deeper, this book is great!

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Handbook of Statistics, Volume 27: Epidemiology and Medical Statistics Review

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(More customer reviews)This series is a long and successful collection of volumes on the most important topics in statistics. Professor Krishnaiah from the University of Pittsburgh was one of the first editors. When the famous Professor C. R. Rao moved from India to the US he joined his colleague Krishnaiah at Pittsburgh and helped coedit some of these handbooks. After Krishnaiah's death Professor Rao continued to edit these volumes and brought in some of his colleagues as co-editors.
This is volume 27 and it contains 27 chapters on the topic of medical statistics and epidemiology. This is very important to me as I work in the medical research area.
The editors have assembly the leading experts in statistical methodology and applied biostatistical reserach to produce these 27 chapters.
Ross Prentice (coauthor of 2 edition of a book on survival analysis and the Cox model) wrote an overview chapter on the topics to be covered. He has had a long career at the Fred Hutchinson Cancer Research Center in Seattle Washington. Fred Hutchinson was a famous manager for the Cincinnati Reds in the early 1960s and brought them to the World Series in 1961. He died of cancer and his estate started an endowment to form this research center.
Don Rubin covers causal analysis in Chapter 2. Rubin is an expert on causal inference, Bayesian methods and missing data. He presents the randomized trial approaches of Fisher and Neyman introduces propensity scores. A Bayesian approach is also covered.
Other chapters cover epidemiologic study designs, assessments for biomarkers, linear and nonlinear regression models, logistic regression, regression models for count data, mixed linear and nonlinear models, survival analysis, competing risk models, cluster analysis, factor analysis, structural equation models, crossover trials, group sequential methods, early phase trials (I and II), late phase (III and IV), missing data, Meta-Analysis, multiple comparisons, sample size determination
statistical learning using splines, evidence based medicine, marginal regression models, use of difference equations in modeling and the Bayesian approach to medical inference.
This volume is over 800 pages and each chapter is fairly long and includes large bibliographies.

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Statistical Methods in Medical Research (Armitage, Statistical Methods in Medical Research) Review

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(More customer reviews)This is definitely one of the most comprehensive of all biostatistics textbooks out there and it is also the best. Authoritative in style, it starts from the very basics and surveys in *detail* almost every method in biostatistics.
You will be impressed: Apart from *all* the basic techniques usually found in most biostatistics texbooks, it has two extensive chapters on Bayesian methods (with Gibbs sampling, MCMC, etc., you name it), extensive treatments of clinical trials, extensive treatments of longitudinal data and GEE models, extensive details on the bootstrap and jackknife, an extensive chapter on methods in epidemiology (risk ratios, Mantel-Haenszel method), extensive treatments of categorical data (contingency tables, logistic regression), and an extensive chapter on survival analysis. This is indeed a very extensive book.
"Statistical Methods in Medical Research" is also a book on methodology, so theorems and proofs are not to be expected. Also there are no exercises, but there are excellent illustrative examples of the various methods. While it is very descriptive, it contains enough mathematics to keep the presentation of concepts *complete*. It is also very up-to-date and has an excellent reference list.
In a nutshell, this is a work of great ambition and vision: it will cater for beginners and masters alike.


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The explanation and implementation of statistical methods for the medical researcher or statistician remains an integral part of modern medical research. This book explains the use of experimental and analytical biostatistics systems. Its accessible style allows it to be used by the non-mathematician as a fundamental component of successful research.
Since the third edition, there have been many developments in statistical techniques. The fourth edition provides the medical statistician with an accessible guide to these techniques and to reflect the extent of their usage in medical research.
The new edition takes a much more comprehensive approach to its subject. There has been a radical reorganization of the text to improve the continuity and cohesion of the presentation and to extend the scope by covering many new ideas now being introduced into the analysis of medical research data. The authors have tried to maintain the modest level of mathematical exposition that characterized the earlier editions, essentially confining the mathematics to the statement of algebraic formulae rather than pursuing mathematical proofs.
Received the Highly Commended Certificate in the Public Health Category of the 2002 BMA Books Competition.

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Principles of Medical Statistics Review

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(More customer reviews)Alvan Feinstein engraved his name in history through great improvements in medical biostatistics and research methodology just to name a couple. His last book (sadly) is a marvelous trip through medical statistics made very down to earth without loosing the scientific background, but rather explaining it in a very concrete and solid way. A wonderful book for anyone who seeks information on medical statistics. A great complement to his research and clinimetrics books.

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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.

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Using and Understanding Medical Statistics Review

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(More customer reviews)For what this book intends, it truly is outstanding. In general, the medical researcher of any kind is at a severe disadvantage in dealing with professional biostatisticians because often the two do not speak a common language. This very readable, interesting and clearly written book gives the scientist enough knowledge to deal with most relatively simple cases in a language that we understand. With the basic principles of medical statistics made comprehensible, we are able to understand many of the simpler software packages well enough to perform basic statistical analyses and, more importantly, are able to converse intelligently with biostatistical professionals and to design studies that will pass muster. Unlike another reviewer, I do not feel that not covering SAS or other software programs is a disadvantage. No book can cover everything, and this book covers the basics and intermediate levels far better than any book I have seen, certainly better than any biostatistics book written by biostatisticians intended for other biostatisticians. This book understands that biostatistics is a tool, not a fundamental discipline, for the vast majority of scientists and is written to them. The book serves as a ready reference and review if one does not perform these analyses on a daily basis. This book should be on the shelf of every life scientist.

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Survival Analysis for Epidemiologic and Medical Research (Practical Guides to Biostatistics and Epidemiology) Review

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(More customer reviews)Ah, the shudder when a professor assigns his own book. But never fear, Steve Selvin, recipient of numerous teaching awards, is not publishing a book to line the faded jeans pocket, or to swell his balding curly head. Rather, it's just a practical way of printing his lecture notes and homeworks. Furthermore, its rare that a top research university professor has time to write textbooks, so the burden falls to the mediocre. Selvin somehow manages to find the time and save us from so many other books that screw up statistics.

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This practical guide shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts such as bias, confounding, independence, and interaction are presented in the context of survival analysis as well as the basic components of a broad range of applications.

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Practical Statistics for Medical Research (Chapman & Hall/CRC Texts in Statistical Science) Review

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(More customer reviews)This is a very well written and popular text on biostatistics. Altman writes for non-statisticians but the book is best suited for those with at least one prior course in statistics and those who have had mathematics through high school algebra. Emphasis is placed on the important practical problems. Good statistical designs and analyses are emphasized. The pitfalls with many published medical articles are discussed in Chapter 16.
I used this book to teach a 20 lecture course to students (engineers, clinicians and computer scientists) at Pacesetter in 1998 and at Biosense Webster in 1999 (both medical device companies that employed me as senior biostatistician). It was a good refresher course for the CRAs and engineers and it helped to make it easier for me to work with them on their statistical problems.
I have also taught a similar course to undergraduate students in the Health Science Department at Cal State Long Beach. Altman's book is a little too advanced to use as a text for that course but I did use it as a reference and covered material in Chapter 16 at the end of the course. Clear discussion of the medical literature is very important to these students and Altman does a great job!


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Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research.Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.

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