2 comments about The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series).

1. An excellent book on the evaluation of medical tests. Includes a lot of information on techniques used for evaluating diagnostic tests. Easily read even by persons with no medical background.



2. 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.

2 comments about Evolution and the Theory of Games.

1. All you want to know about theory of games



2. With this book, John Maynard Smith initiated a major strand of modern theoretical biology! The book does not require sophisticated mathematical preparation, but it operates on a consistently high level of analytical rigor. It is also very nicely written, with lots of biological examples.

5 comments about Recursive Partitioning and Applications (Statistics for Biology and Health).

1. Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.



2. Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.



3. Zhang and Singer have done a splendid job of explaining recursive partitioning, a topic that should be of great interest to anyone who wants to make sense of data in which there are many potentially important variables contributing to some outcome or variable of interest. One should not be put off by the "... in the Health Sciences" part of the book's title; the potential audience of readers who can benefit from reading it is much greater than this implies (I'm an ecologist, for example). Why? First, because the topics covered have wide applicability in many fields; and second, because the writing is exceptionally clear and easy to follow. If you are able to use a typical introductory text on multiple regression, for example, you should have no difficulty getting a lot out of Zhang and Singer. If you are able to handle a mathematically rigorous approach to statistics but are new to the topics covered here, this book will provide an excellent starting place before you jump into the many references to the recent literature provided by the authors.



4. Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning. There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.



5. Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning.
   
   There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.
   
   It is a little more difficult to read then CART. CART was motivated by biomedical problems but the book covered other applications in business and pattern recognition as well. This texts puts an emphasis on the important medical applications.

No comments about Statistics Toolkit.

No comments about The Statistical Analysis of Functional MRI Data (Statistics for Biology and Health).

1 comments about Data Analysis: An Introduction (Quantitative Applications in the Social Sciences).

1. As a Phd student in sociology, I find this booklet indispensable as a quick and easy reference guide to all the basics of social science statistical analysis. After going through more complicated texts such as Lewis-Beck's "Applied Regression: an Introduction" and Berry and Feldman's "Multiple Regression in Practice", I find this small booklet extremely helpful and clear in its explanations of the logic of basic bivariate and multiple regression analysis, as well as significance testing. It is quite useful as a tool to review the basic methods of analysis, and I especially like the list of regression assumptions at the end, all of which we had to memorize and explain for qualifying exams! I would only request a separate section and more detailed explanation of the Chi-square test, complete with application examples.

5 comments about An Introduction to Medical Statistics (Oxford Medical Publications).

1. This is the second edition of an excellent introductory text for medical students similar to Doug Altman's. Bland covers the basics: designing clinical trials and epidemiological studies, data collection, summarizing data, basic probability, standard errors and confidence intervals along with tests of significance (both parametric and nonparametric). Various types of regression methods are covered and this edition adds Cox proportional hazards models and meta-analysis. Many exercises and examples are taken from Bland's 20 years of experience as a medical statistician. It is noteworthy for dealing with sample size issues in Chapter 18. Mortality statistics and life tables are covered in Chapter 16.

   The book is not quite as advanced as Altman's.




2. Clearly and logically set out starting with the reasons for medical statistics and how studies are designed and moving logically to the methods used. All practicing doctors need to know something about medical statistics. This is a very good place to start. It is written for non-mathematicians but has enough mathematics to justify the information presented and stimulate further interest. It is a relatively short book with clear logical explanations rather than flashy illustrations and computer graphics. Also provides exercises, essential in all mathematical subjects.



3. It's an okay text but the author quotes himself a lot. I just wish all the tables and formulas were in a specific location and the symbols for the formulas defined each time a new one was presented.



4. Seems a good basic sufficent stand-alone stats book for medical professional (ie non specialised statistician), although admittedly I haven't used many others to compare, covering a comprehensive cross section of the commonly used medical science stats techniques, with excellent basic maths / stats principles that are relatively easy to understand for those with a basic mathematics background (higher secondary school). Possibly too light on for some more complex stat techniques (eg multi-variate analysis - eg no mention of cluster or hierarchial or other specifically indicated analysis etc.) - ie newer edition mayn't have moved sufficiently with the times. Nonetheless a good solid stats background, very readable, and responsible in imploring appropiate use of stats. Very usable reference and / or learning book.



5. This is the second edition of an excellent introductory text for medical students similar to Doug Altman's. Bland covers the basics: designing clinical trials and epidemiological studies, data collection, summarizing data, basic probability, standard errors and confidence intervals along with tests of significance (both parametric and nonparametric). Various types of regression methods are covered and this edition adds Cox proportional hazards models and meta-analysis. Many exercises and examples are taken from Bland's 20 years of experience as a medical statistician. It is noteworthy for dealing with sample size issues in Chapter 18. Mortality statistics and life tables are covered in Chapter 16.
   The book is not quite as advanced as Altman's.

1 comments about Clinical Trials: A Practical Approach.

1. I found this book almost 15 years ago when first designing a series of clinical trials for cytomegaovirus prevention in organ transplantation. It is a very practical "how to" volume, extremely well written and concise. I recommend this to all clinical investigators who are starting out, and for all trainees. I have been waiting for his revised edition for over a decade and only hope he will update this great book.

1 comments about Study Design and Statistical Analysis: A Practical Guide for Clinicians.

1. I think It is a good support for clinicians who are intented to know statistical bases before submission of a scientific paper.

2 comments about Dicing with Death: Chance, Risk and Health.

1. Senn provides a broad introduction to key statistical ideas relevant to medicine and epidemiology. He switches effortlessly between basic concepts such as hypothesis testing, standard error and conditional probabilities to deep philosophy such as Bayesian versus frequentist schools, irrationality of induction and the use of meta-analysis. His style is highly readable mixing technical content with historical anecdotes and startling digressions.
   
   The ideal reader is someone who has a decent background in statistics, such as that gained in a university-level statistics course.
   
   The key strengths of the book include: (1) clear, lucid explanation of many concepts including modern ones not typically covered in first courses -- for instance, the contrast between Bayesian and frequentist approaches is done much better here than in most other books; (2) about 1/2-1/3 of the examples come from the real world (the remainder split between coin tossing experiments and hypotheticals), which is a high proportion among this sort of books; (3) good discussion of intuition and reasoning as opposed to just formulae.
   
   The weaknesses (quibbles) include: (1) Senn's penchant for puns and word play provides humor but can get in the way of understanding the material; (2) his frequent digressions leave a host of loose ends and dead ends, which can frustrate some readers but for others, this strategy reveals exciting avenues for further exploration.
   
   This is really a great book for someone who knows some statistics and really want to dig much deeper into the intuition and philosophy behind this field.



2. Excellent! The focus is on statistics in medicine, but the book zigzags through recent issues (ethics and politics of clinical trials, lawyer's abuse of statistical evidence, vaccine scares), sometimes sophisticated analysis of particular data, combined with explanation and history of basic concepts, with half-page biographies of historical and modern statisticians going far beyond the usual suspects. Has the lively style of The Economist, addressing a mentally alert adult reader rather than a casual reader or bored student. In particular, readers who have taken one course in statistics will get a view of "the big picture", and this is the best single book for that purpose.