5 comments about How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Reviewers.

1. As a medical writer and editor at The Cleveland Clinic, Thomas A. Lang found that the lack of clear understanding of statistics by non-statisticians affected the clarity of their writing. Physicians had the same problem while writing up their research papers for publication. Lang perceived a need among medical and science writers to understand just enough of biostatistics to make them better writers and editors without becoming statisticians themselves. He devised workshops that were conducted by the American Medical Writers Association which were enormously successful. The logical next step was to write this book based on the valuable teaching experience and feedback he got at those courses. In other words, this is a book that wasn't written in a vacuum but is the result of a perceived need, and the author's experience in meeting that need. Co-author Michelle Secic has also contributed with her expertise, making it a valuable book for people in this field.



2. I am glad to read this book before I prepared for my statistical clinical studies. This book could give you basic ideas, but you need to discuss with your collegues or use more advanced books to improve further.



3. The material is presented in a way that will help medical writers understand clinical trials more fully, and answer questions that come while writing.



4. This is the best reference book I have found on the topic of how to present data for manuscripts to be submitted to peer-reviewed journals. It is easy to use, super easy to understand, and covers all elements of the manuscript preparation process as far as data representation is concerned. I highly recommend it for anyone looking for a book on the subject. It includes many examples of data tables, figures, and even examples of how to write up titles and data descriptions.



5. I found this book unexpectedly useful, complete and easy to read. It contains the kind of statistical knowledge that medical people do like to see. It is not another textbook that reviews medical statistics. However it provides the clues to understand and classify all these statistical jargon and processes that cause difficulties to eveybody that loves to handle statistics himself.I fully recommend it

2 comments about Intermediate Statistics: A Modern Approach.

1. The author of this book does not explain this information in a way that's easy to understand. Explanations are too short, and unclear. Some of the examples used to explain the formulas are incorrect.



2. This is yet another well written book by Stevens. It goes into a sufficient amount of detail to understand the how, when, why, and where of these statistical analyses. One does not have to be a statistician to understand it. This book has great utility. The one disappointment was the lack of updated SPSS syntax, but other than this, it is well worth every penny.

1 comments about Chronic Disease Epidemiology and Control.

1. this newly edited book presents a comprehensive oveeview of the field of chronic diseases with epidemiology and their control.

5 comments about Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health).

1. A very specific book, with a lot of details for a statistitian



2. This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.



3. The authors say that they created this book to fit with a course they taught at UC San Francisco to medical students. The book is very sophisticated and a great reference source for practicing biostatisticians in industry or research. It surprises me a little that they find it effective for there non-technical audience. Although the topics are technical and many are advanced they do cover it in a conceptual way without heavy mathematics but still requiring some statistics classes as prerequisite.
   
   Regression does not cover all the techniques of biostatistics but as the authors point out the four topics in the subtitle are among the most important. I know this from my many years of experience as a bisostatistician in the medical device and pharmaceutical industries. They use many good practical examples useing many of the common variables studies in many clinical trials where physical exams are given to record blood pressure and other vital signs and chemistry labs are done to determine cholesterol levels and other things that can be factors in various diseases. Also glucose levels are very important to monitor for diabetes trials.
   
   In addition to the standard topics general estimating equations and generalized linear models are covered and where appropriate bootstrap confidence intervals. There is even a chapter on complex surveys a topic important when quality of life is an endpoint and survey instruments are used to measure it.
   
   In the survival analysis chapter the Kaplan-Meier curves, log rank tests and Cox proportional hazards models are covered as expected but the authors go further to include extensions of the Cox model when the proportional hazards assumption fails. My only disappointment is that there is no coverage of actuarial life tables. At the medical device companies that I worked for it was common to get interval data on events rather than continuous data and then the Cutler-Ederer life table method is the analog for interval data to the Kaplan-Meier estimator for continuous data.
   
   The book covers many topics but is concise as the authors claim. The authors provide a lot of examples that they work out using the statistical package Stata. The authors claim that Stata is the package of choice for biostatistics. This may be the case in academic settings but is certainly not the case in the pharmaceutical industry where SAS is used almost exclusively. I think that it would have been better to show how to write the computer code for solving these problems both in SAS and Stata. To the authors credit Stat is a very good package for their purpose and they do at times mention SAS and SPSS which are the other two major statistical packages used in industry.
   
   All in all this is a very good book that is worth its list price. I will use it as a reference. it also contains a very nice bibliography of 9 pages.



4. That is exactly what the title promises. High yield introduction to clinically applied regression methods. A marvel of a book for the subject.



5. I have owned this book for a couple of weeks. In that short time it has proven very useful to me.
   
   The authors use an easy-to-follow writing style and don't get too bogged down in theoretical, statistical formulas. It is full of useful figures that illustrate the points being made. Note: although the authors rely on Stata for creating their printouts and figures, this is not a book on how to use Stata. You don't get the feeling that you have to learn Stata in order to follow along. I have found that most of the Stata diagrams are very similar to the diagrams created in SPSS, and probably SAS and R for that matter.
   
   Although I am reading the book from beginning to end, I have already gleaned some useful information from advanced chapters, thus suggesting that it is a good reference book. For instance, I was frustrated by the lack of coverage on interpreting log transformed data (in multiple regression) in other stats books. I was pleased to discover that this book covers this issue in a clear and concise manner. I am also pleased that the authors have included a chapter on generalized linear models.
   
   This is a very good book for people working in health care research. The authors talk to the reader and explain things in a lucid manner (I have read several stats books that do not do this, so it is a refreshing change). The authors also provide many practical examples to clarify the issues. A background in the basics of statistics is required.

5 comments about Survival Analysis.

1. In this new edition, most of the errata are corrected and the texts are explained in a more detailed way.

   The formulae are correct and the examples are explained in a more direct and expressive way than that in the 1st edition.

   The most valuable one is its Theoretical Notes and Practical Notes. They show a lot of different points of views.

   A good-buy and must-read for those want to have an intense level in Survival Analysis. Suitable for elementary and intermediate candidates to read and study.

   Ian Lauder




2. This textbook is too heavy on mathematical theory, and as a result ends up being largely uninformative. It is also long-winded to the point of being interminable. In order to implement survival analysis techniques, the practicing statistician does not need to wade through endless proofs, derivations, and digressions into the specification of likelihood functions. There are many textbooks available that provide a more intuitive understanding of survival analysis techniques, in a much shorter space.



3. I am a computer scientist and using this book for my research to address a problem. This book is well written but of course target audience are people with solid background in probability theory and parameteric estimation (pattern recognition). Therefore please do not expect that author will teach you basic probability theory. Contents are more applied in nature therefore natural audience are staticians and researchers.



4. I used this book for a class in survival analysis (a graduate level biostats course) and I found it very useful. Much of the first several chapters are fairly quick relative to many graduate statistics texts and focuses on application with less emphasis on theory. Overall, I have no major qualms with the book. The author goes on a bit longer than necessary but I'd rather end up skimming text than be stuck deciphering terse material. This extra explanation also opens the book up to a wider audience.
   
   A solid understanding of basic statistics is necessary to get started in this book. To get more, 4+ semester-long statistics courses, at least one based in regression, would be ideal. A basic knowledge in mathematical analysis as it pertains to statistics (mainly dealing with convergence in law) will be beneficial to understanding some of the intricacies of the topics and answer many of the 'whys'.
   
   In conjunction with the course and the book, I worked problems in R with the 'survival' package, which I found very useful. (R is a free statistical program. A basic understanding of R would be necessary before trying to use the survival package -- I would recommend Dalgaard's book for an intro to R if this is of interest.) I have a good understanding of R and found the survival package documentation supplemented by rseek . org searches (when I got stuck) sufficient to figure out how to implement the survival functions in R.
   
   On the example setup and problems...
   at the end of each chapter, this book is a bit hit-or-miss. Some problems are good. Many are not. There is a lot of confusion created by some of the problems, which leads into the part of the book I take the most issue with. The authors refer to scattered examples in problems (take for example, referring to example 8.3 in problem 9.5). The thing is, Example 8.3 starts on page 251 and then it continues randomly throughout the remainder of the chapter until page 274 (I had to page through the chapter to find those page numbers). The examples in mid-to-late chapters can be very scatter-brained and some of the problems start to become this way as well. The authors seem to forget that keeping track of the 15-20 studies they use in this text is no small task and that they've spent a lot more time looking at them than others. Self-contained examples where I don't need to flip back to chapter 1 or some other example to read about the study would be really nice. The examples and problems could have been much more user-friendly to accelerate the learning process.



5. The authors present an intermediate level text on survival analysis introducing the concepts and techniques and providing many real examples. Covers all the standard methods including the Cox proportional hazard model (with stratification) and some methods not commonly covered including regression diagnostics and multivariate survival methods (including fraility models).

2 comments about Risk Society: Towards a New Modernity (Published in association with Theory, Culture & Society).

1. The Book consists of two interrelated theses. one reflexive modernization and other issue of risk. The concept of risk is directly bound to the concept of reflexive modernization.It shows how classical modernization is different from reflexive modernization. Risk may be defined as a systematic way of dealing with hazards and insecurities induced and introduced by modernization itself.The risk and hazards of today is of global nature of their threat to people,animals and plants .It results in global industrial pollution,contamination of air,water,foodstuffs etc. and also sickness ,death of plants, animals and people. Another dimension of risk is social transformation with modernity.There is a process of individualization deprive class distints but inequalities by no means disappear.There is a shift from the system of standardised full employment to the system of flexible and pluralised under employment.Risk of scientific development increases disproportionately faster than solution. With the globalisation of industrial society political system looses its function as modernization, technoeconomic system changes the realms of social life and on other hand political systens pre-supposes condition of the system.



2. Ulrich Beck argues that industrial society that used to be known as the distribution of goods has now been moved toward distributions of risk and hazard named as risk society. In other words in the advanced modern world, the social production of wealth systematically goes hand in hand with social production of risks. Accordingly, the problems and conflicts of distribution in a society of shortages are over layered by problems and conflicts that arise from over-production, definition and distribution of scientifically and technologically produced risks, says Beck. Then He argues that science has changed from an activity in the service of truth to an activity without truth. Likewise the consequences of this reflexive modernization contain a tendency toward globalization and ignore the boundaries of nation states which lastly results in continuous global endangerment.
   
   The book also describes the way that the general forms of social life has moved from traditional societies in the mid 18th century to early modernity in 20th century and lastly toward reflexive modernity in the late 20th century which individualism is widespread. No longer, unskilled and uneducated people required and instead of high value on long term loyalty to the corporate institutions of the 20th century a shift to the self as the primary agent (i.e. a shift to I) had taken place. To sum up, Beck gives a new identity to risk which long has been dominated by rational doctrine. Manufactured Risks which are at the heart of the Modern society have become a taboo. The modern corporations have build a "Family of Myths" among which the most important is the "Myth of Rationality".

5 comments about Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics).

1. Applied Survival Analysis is an excellent book for someone seeking a non-mathematicial explanation of survival analysis. The book covers the motivation behind the development of survival analysis, estimation of survival curves, the Cox proportionial hazards, and some parametric models. The book also covers the major methods used in variable selection, model building, and diagnostics. Someone with an undergraduate background in statistics and econometrics will understand the book. The book relies on text to discuss the methods and uses mathematical formulas only when absolutely necessary. Numerous examples are used to highlight what the text covers. The math that is used is easily understandable. This book is ideal for someone who needs to learn the tools of survival analysis but not how they were derived.



2. Hosmer and Lemeshow have given us a clear, nontechnical introduction to using survival models. The book strikes a good balance between covering the basics and addressing the most recent, state-of-the-art techniques, including repeated events, frailty models, and others. They also do a good job of addressing practical issues, including estimation details and available software. While most of the examples are drawn from medicine and biostatistics, this book could also serve as a useful starting point for social and behavioral scientists interesting in learning the fundamentals of these models, as well as a useful reference for applied researchers.



3. I enjoyed the authors' book on logistic regression analysis in 1989, and this book is just as good, or better, with many extremely practical suggestions on building regression models for survival data. Happily, the authors summarize, compare, and contrast several major texts on survival analysis which have appeared in the past 10 years. For example, they discuss different names used by different authors for score residuals. They present a helpful appendix on the counting process approach to survival analysis, which will make more advanced texts accessible to students; thus, anyone who wants to use survival analysis, at any level, should consult this book, even if he has already studied books by Miller, Lee, Collett, Fleming-Harington,Andersen, et al, etc. An unfortunate drawback to this book is that the first printing contains many careless errors, some of which may affect student learning: for example, the definition of a survival function is misstated. I recommend that you insist on the second or third printing when buying this book, and you will be quite satisfied.



4. This book provides a good, clear, concise explanation of Cox's proportional hazards models. For someone seeking a non-mathematical description this is a great guide. The original datasets from the text examples can even be downloaded and you can go through the same process yourself. Because of some mistakes in the text, I would recomend looking at other sources as well.



5. The authors provide a really nice, non-technical survey of the landscape for Cox Proportional Hazards models. A nice aspect of their treatment is the care they take to reference all highly technical texts and journal articles. For example, if you'd like to find out more about goodness-of-fit tests for survival models, the authors provide ample references to the Counting Process Theory of Martingale Residuals.
   
   The first chapter discusses the basic characteristics of survival data, including the notion of censoring (in all of its various forms). Examples of the principle types of censoring are included. The chapter also includes introductory material on the general survival model, including a nice description of the log likelihood function. Curiously, the rigorous definition of the hazard function has been omitted, probably to avoid intimidating readers who are not familiar with formal limits.
   
   Chapter 2 continues to build up the general survival model and introduces the relationship between the survivor function and the cumulative hazard. Pointwise estimators for the survivor function are discussed, including the Kaplan-Meier estimator along with the various variance estimators. Test statistics for comparing two survival populations are introduced, including the Log-Rank and General Wilcoxon statistics. The reader is encouraged to read the counting process treatments of these statistics to see why they produced defensible hypothesis tests.
   
   Chapter 3 is devoted to the Cox Model and Cox's partial likelihood function. Tests for significance of the coefficients are introduced, included the Wald test, log likelihood ratio test and the score test. These are used heavily in the later chapters as the basis of a model-building methodology.
   
   Chapter 4 is a very short, but nicely written chapter explaining how to interpret the values of each regression coefficent. It also describes covariate-adjustment techniques for model diagnostics.
   
   Chapter 5 is just a wonderful chapter which outlines classical model building techniques. This is a great chapter for anyone who has ever been thrown a ton of data (with a bushel of possible covariates) and asked to "fit a model to this stuff".
   Readers who have done a lot of purposeful fitting of linear regression models won't find the basic techniques new, but use of survival specific residuals and selection criterion will probably be an eye-opener. The section on assessing the functional form for continuous covariates is also nicely written.
   However, the section on Best Subsets Selection was a little too "cook-booky" for my taste.
   
   Chapter 6 is another very nice chapter on goodness-of-fit. It discusses analysis of the various residuals and their use for analysis outliers, testing proportional hazards assumptions and overall Goodness-of-Fit.
   
   Chapter 7 discusses the standard extensions of the Cox model, including stratification and time-varying covariates. Chapter 8 discusses parametric survival models, and is a good introduction to the SAS procedure LIFEREG. The generalization of the Cox model to recurring event data (also know as Aalen's multiplicative intensity model) can be found in Chapter 9.
   
   My only complaint is that each chapter was designed to be read in one sitting. Individual ideas, topics and formulas can be buried in a seemingly unbroken chain of paragraphs. The lack of sub-sub section titles,etc, makes using the text as is somewhat cumbersome to use as a desk reference. I've gotten around this limitation by marking key concepts, etc., in the margin in order to give a "quick search" capability enhancement to the index.

1 comments about Medical Epidemiology (Lange Basic Science).

1. I've used this and the previous edition of Greenberg's text for my epidemiology and literature interpretation course for Physician Assistant students. I've found the text very readable and nicely linked to clinical practice. Each chapter starts with a clinical case around which the chapter is focused. The study questions at the end of each chapter also are useful.

   It is an excellent introductory text for clinicians/health professional students. Probably would not be the best choice for a epidemiology course in an epidemiology graduate program or career epidemiologists.

5 comments about Pdq Statistics (PDQ Series) Third Edition (PDQ).

1. I've spent years looking for a book on statistics that is lucid for professionals who need to use statistical methods but who don't want to get a Ph.D. in math to be able to undertand them.

   This book is the best. No incomprehensible Greek formulas. No tortured prose. And yet it's not just a mere introduction of what a mode is. It includes some advanced statistical methods and gives you all you need to know to understand what they do and how and when you would use them. It is long enough to describe each method sufficiently, including pitfalls and things to watch out for. Yet the book is short enough that reading it and undertanding the whole thing is possible in a modest length of time.

   You can stop searching now. This is the book.




2. This book is the only statistics book of its type. For each section covering a specific statistical method (from simple methods to those you may not even cover in your PhD training), a concise 2-5 page summary is presented. The goal is not to enable the reader to calculate any of these statistics, but to understand conceptually what each statistic means. This is where it can fill in information other statistics texts never get to. A student (or researcher!) who can churn out factorial ANOVA results, but doesn't truly understand what they mean can turn to this book for clarity. It's simple (for statistics), it's short, it's clear, and you have to love a book that is dedicated "To the many people who have made this book both possible and necessary -- authors of other statistics books"!



3. In these times, you can't walk in the streets without hear or read some statistics, in the paper, in the market or everywhere.
   This is the book not only for a biomedical student, is a book for anyone interested in know what is statistics about. The politicians and doctor can lie with statistics, but with the knowledge from this book, you can see beyond. The use of examples is extensive, and some of them are hilarious. It's a book very concise, informative and, the best of all, funny.



4. I must begin by stating that I didn't receive the book until the week of my exam. :( So, obviously I was not reliant on it. The book is small enough to be "not so" intimidating. It has relevant formulas and explanations to give one the gist of stats needed to pass a biostats class.
   
   My opinion of the book may have been better had the product arrived sooner.



5. I have a couple of other fat, authoratative biostats books but PDQ is my reference of choice. If you want to understand biostats, you can't lose with this clever concise text.

1 comments about Social Epidemiology.

1. Good overview but lacks clear writing style overall. Tedious to read at times and therefore a disappointment for the MPH student being introduced to this burgeoning field.