Clinical-Trials Books

I own several books on clinical trials and this one is my favorite. It is biblical in its treatment of the topic and always seems to contain what my other books don't.

There are a few strengths that are particularly worth pointing out:
1) Makes many references to regulatory guidelines.
2) Excellent coverage of the various trial designs.
3) Good sample size chapter.
4) Several chapters on how to practically implement a trial.

Other options include:
-Piantodosi (Clinical Trials: methodologic perspective): my second favorite, not as comprehensive as Chow and Liu
-Freidman and DeMets (Fundamentals of Clinical Trials): a bit too superficial but very well written
-Pocock (Clinical trials: practical approach): a bit dated and superficial

I selected this text for a new doctoral level course on clinical trials based mostly on the topics covered. When we started using it, I found many errors in logical thinking. So far, one of the worst is a view that randomization satisfies some statistical assumption. It does not. Its function is to make groups similar, on the average. It is not in any way a substitute for random sampling. Separating these two random processes is basic to understanding clinical trials. I am embarrassed that I had my students purchase this book.

As a newcomer to the pharmaceutical industry, this book has helped me to understand the jargon that is everyday language for clinical research associates. Explanations are presented simply.

This is the sort of book that one might expect to receive as a "freebee" from a drug company representative. For $..., it is very overpriced. Considering that for around the same amount of money, one can buy a very thorough medical dictionary, this "book" falls short. Save your money and apply it towards a "real" text.

The first 19 pages were missing and an email to editor didn't get an answer. sad since the book is well written, good as an introduction but also for professionals.

I've always been opposed to anti-psychiatry and Thomas Szasz seems to be its most prominent spokesman. I read this book simply to understand more of the opposition; know the enemy, so to speak.

The book is primarily concerned with the issue of involuntary confinement in state mental hospitals, the legality and the morality of this. When this book was written most states had either begun or were in the process of releasing a great many of their state mental hospital patients in what has been called deinstitutionalization. Now very few state mental hospitals exist.

Szasz's arguments are, at times, cogent and respectable. His empathy for humankind and freedom and America as a free nation is attractive and made of the very best intentions. This book was not concerned with the veracity of mental illness as an illness (which Szasz has questioned in his other books) but instead only with involuntary confinement - or simply confinement - in state mental hospitals - or any hospitals. He likens the involuntary committed mental hospital patients as slaves in the way blacks were before slavery was completely outlawed in the Constitution. Now he wants the same thing for psychiatry - ban it, outlaw it, throw it away.

Szasz is using the guilt-trip method of argument, so to speak. He's yelling at his audience through the paper to get up and do something about this inhumanity because after all if you don't, you're to blame. His writing style is not academic but radical, angry. Time after time he refers to psychiatrists as performing "mad-doctoring" of being "wardens" in the state mental hospital which, in his estimation, is a prison. He tares apart the American Psychiatric Association on one page, and the American Civil Liberties Union on the next.

Szasz, who is a fully trained psychiatrist, was popular because he was seen as an expert, and not just a fool with an opinion. These days his views are seen as insincere, smug and impractical. And, the Church of Scientology, which has become enlivened in recent years with its celebrity converts, uses Szasz's many arguments on the falsehood of mental illness to promote their own agenda.

Speaking from how well Szasz presents his argument, his writing and so forth, the book is not especially bad. There are some good points made about state mental hospitals involuntarily committing patients, which the American public must be made aware of. And they were, because at this present time well over 90% of all state mental hospital patients have been released. Psychiatry is done on an outpatient basis nowadays, and most mentally ill people prefer this and get benefit from this. Although I cannot recommend this book; ideologically speaking, it'd be like a Jew recommending Mein Kampf. I can't do it and I won't do it.

In his book, Psychiatric Slavery, Szasz makes the comparison of involuntary institutionalization and slavery. The book primarily discusses the case of Kenneth Donaldson, which was a Supreme Court case on the right of treatment. The book discusses the various briefs of the case, elaborating on various issues.

Donaldson was institutionalized because he told his parents that the neighbors were trying to poison him. Although his parents sent him to an asylum for this, Donaldson maintained he was not mentally disturbed.

The American Civil Liberties Union (ACLU) assisted Donaldson in attempting to gain his freedom, but in a bizarre way. Although Donaldson wanted nothing to do with medication and shock therapy, he sued the hospital director because he was not receiving treatment. From the data presented, I got the impression that the ACLU used Donaldson for their own ends, which was to strengthen the power of psychiatry.

I completely disagree with the previous review. I have done basic and clinical research for the past 10 years, have published, and have also taught medical students, residents and fellows how to analyze data.
I have come to understand that there is no one "perfect" book on statistics. For example, I own probably around 15 books on various topics in statistics. Why? Because different authors did best with different ways of presenting the material, explanations, and topics themselves. This book is no exception. There are some good things, there are some things that could have been clearer. Where this book wins is if you need a source to quickly look something up - it is a definite thumbs-up. But if you need to "learn" then buy other texts.

Over the past few years I have studied many statistics books. This is one of the worst. The authors themselves picked up four errors in a stand alone erratum page; but there are many more. On page 14, they describe the test statistic as the Mean/Standard error. Instead it is the Difference in Mean/Standard error. On page 25, their taxonomy of tests is incorrect. They suggest the Mann-Whitney test for paired testing instead of unpaired testing. On page 30, the calculation of the Chi Square test is incorrect. On page 53, the calculation of incurring a type I error when testing for multiple hypotheses is complicated and incorrect (it should be simply 1 - Alpha at the nth power where n is number of hypotheses tested). On page 73, when dealing with non normal data they recommend the Tau test for correlation when the standard one is Spearman Rank correlation.

Explanations of tests are either Byzantine or absent. On page 27, the way they set up the Wilcoxon Rank Sum test is so unorthodox as to make it incomprehensible. On page 54, the output of four different post ANOVA tests are shown and extensively mentioned in the narrative. But, there are no explanation whatsoever on the underlying test calculations.

The above caused me to distrust everything about this book. If I picked up so many mistakes within the material I know, how many more are there among the material I don't know.

The purpose of reading statistics books is to acquire reliable knowledge. That book failed on this count. If you are interested in the underlying statistics of clinical trials I recommend instead Intuitive Biostatistics. This is an excellent and occasionally challenging book. Be sure you have a good foundation in basic statistics before studying it. For basic statistics I recommend Forgotten Statistics.

I don't think the author has enough experience in the Data Management field.

The authors gave an excellent overview af scientific skills used in the field of clinical research.

The methodologies described are out of date. The descriptions are inadequate.

I have to disagree with the other reviewers here. This is exactly the kind of guide for which I was looking. New to the clinical research field (but not to information management), I wanted that "missing manual" for data management from this field's unique perspective and needs. I've looked at a lot of texts (both on Amazon and in academic libraries) and was surprised that there weren't more books like this out there. Something that is basic, that starts with the nuts & bolts of clinical data management. Yes, of course, working in the field will help tremendously. And I'm sure there are more in-depth books out there that take these concepts and add layers of complexity. But this gives you a really good overview with a solid foundation. I'm currently using a library copy, but am seriously considering buying one for my own library & reference. The price is steep, but it's market economics -- there's little or no competition for this type of manual and the publisher & author apparently know that. Pure supply & demand.

Sometimes it's hard to believe to find a good book in clinical data management. I read this book, and found it not worth the $$$. I would say try to get a job in DM, then after 1 yr, you will know much more than this book.

From the editorial comments this book seemed exactly what I was looking for: "covering the basic data management tasks that all data managers must understand, focusing on the computer systems, including electronic data".
However, it is a collection of facts and bulleted lists of things to do while conducting a clinical trial and I still do not find a single tangible example. This makes all that is written impossible to grasp and understand.
Also, have you ever seen a book without a reference list? Well, this is the first book I have ever read that is not a fiction book that does not have a single reference nor a reference list at the end of the book or of each chapter. This makes the book almost completely useless.
One could pardon typographical errors such as "this data" if the book was better. In this context, this kind of typos only give an extended idea of the bad quality of this book.
I feel as if I wasted my money and time reading it.

Although billed as a 'primer for health & biomedical professionals', this book is actually too basic for that. Most in the field are not undergrads, and so need the more advanced statistical measuring methods explained more thoroughly (or, in some cases, at all). There is nothing about infective disease, so those measures are also lacking.

This book feels very disorganized, with little help from the index and contents. There are much better books of this type on the market - for example, Primer of Biostatistics by Stanton A. Glantz or even Epidemiology by Leon Gordis. I'd keep looking.

El libro contiene información básica para diseñar ensayos clínicos. Una buena guía para principiantes en estos temas.

From whichever perspective you look at it - this books offers absolutely nothing. I wish I could get my $$ back!

I don't know why Dr. Chow put this garbage in his series.

The author has an MD from China and a PhD from Louisiana State University Medical Center. However, his understanding of probability and statistics is less than what would be expected from a person with a Ph.D. in biostatistics. The book has one theme. It rejects the use of classical statistical methods of hypothesis testing that began with Neyman and Pearson's work in the 1930s. Although the author claims that he is rejecting it in the context of clincial trials, it appears that his strong statements indicate that he believes it is not very useful and provides no information to draw conclusions. His arguments, provided most strikingly in Chapter 10 "The Fiction behind the Current Statistics and Its Consequences", reveal this and best display his ignorance.

It is true that most clinical trials are conducted on restricted sets of subjects who must meet carefully planned inclusion/exclusion criteria. Also, clinical centers are chosen by the sponsor (often a pharmaceutical company) and are picked because they have performed well in the past or have a well known investigator. Therefore, the common paradigm of statistical inference that the subjects are a random sample from a larger population is not even closely true. This seems to be a good argument for not taking formal statistical hypothesis testing too seriously in this context and too much attention has been placed on p-values and rigid decision making in the regulatory arena so I was very much interested in seeing how Norleans would get around these problems.

But instead of recognizing the inadequacies of the models and finding better models, he resorts to a subjective form of decision making based solely on graphics and a misunderstanding of methods such as maximum likelihood and analysis of variance. These problems require increased sophistication for solution not the abandonment of statistical modeling.

Graphical techniques are clearly useful and the pioneering work of Tukey led to a deeper appreciation for good exploratory data analysis techniques beginning in the 1960s. However, Norleans ignores or is ignorant of this literature as he fails to use any of the techniques of Tukey. Rather he concentrates on unconventional and not always very informative graphical displays. These techniques seem to be designed to look at individuals more than groups and can often be very cluttered. There is no reference to Tukey, Cleveland or Tufte, the pioneers in statistical graphics.

I find his condemnation of Jerzy Neyman particularly insulting and it shows both naivety and lack of understanding. He doesn't even know how to spell Neyman's first name (sic Jersey)! He claims that statistical hypothesis testing is based on assuming the truth can be known when in fact it is just the opposite. The framework assumes that there is a true state of nature but the truth can never be known and that we can only express our degree of belief in probabilities that particular decision rules lead to incorrect conclusions. Sample size requirements and decisions are made when these error probabilities are sufficiently small.

Neyman-Pearson theory has also been criticized for using a sharp null hypothesis but extensions of the theory got around that problem as can be seen from the famous text by Lehmann on hypothesis testing in the late 1950s. It is commonplace now to use both composite null and alternative hypotheses and appropriate methods for equivalence teting have been devised by switching the usual null and alternative hypothesis. But Norleans appears to be unaware of these advances. Also the generalization by Wald and others to statistical decision theory based on loss functions or utility functions is likewise overlooked.

On the one hand he reject parametric statistical inference because he does not believe in the use of parametric distributions to represent test statistics but yet he accepts the method of maximum likelihood which he does not recognize as parametric. But maximum likelihood methods are not always robust and in some cases not even sensible.

The analysis of clinical trials is challenging. Mixed effects models, censored survival models, handling of missing data, multiplicity adjustment are among the many tools and issues associated with these problems. Probability and statistics have subtleties that cannot always be simplified. It takes sophistication and the clever use of probability to conquer these problems but Norleans offers us none of this.

He appears to be ignorant of the asymptotic theory of statistics which is based on convergence concepts from probability. The only asymptotic result he mentions is the central limit theorem and that he seems to think is based on a Taylor series approximation. With the Poisson model he mentions the problem of overdispersion but instead of recognizing that with medical data more complex models such as compound Poisson can adequate address the issue and make sense clinically he rejects the methodology itself.

Medical researchers who want to understand statistics and its useful role in medicine and other research would be better served by reading David Salsburg's "A Lady Tasting Tea" than the garbage in this book.

I really have serious issue about Dr. Chow's selection of this book for this series and I cannot understand how he can characterize it so favorably in his introduction.

In 2001 under the pen name statman13 I wrote the first amazon customer review of this book. It was a very detailed review and one of the very few reviews that I have written that really panned a book. I am happy to see that in the intevening time two other reviewers expressed similar negative opinions. Unfortunately if they bought the book they must not have read mine! In 2001 I bought the book at a "discount price" because the author was presenting the work as a tutorial at the Deming conference, the title interested me and the abstract was intriguing. The book was just coming out. So I had no reviews to base a purchasing decision on and I knew that the series editor had a great reputation as an author and editor on these topics. I was on the conference organizing committee and although I had not been the member who invited Mark, I was duped into buying the book and I agreed to moderate his session. The session was an embarrassment. It was not a tutorial but a diatribe against classical statistics. The author carrying degrees in both medicine and statistics simply demonstrated his ignorance of statistics and presented his own nonsense methods. I can't really blame my colleague who pick him for the tutorial as he was duped just the way I was. It is unusual for the Deming conference to have such a terrible presentation. All the rest of the tutorials and short courses were excellent as they have been before and since and Walter Young and his committee volunteers deserve congratulations over the consistent high quality and state-of-the-art presentations. I served on the committee in the past and have given tutorials there also.

Because it is so easy for prospective buyers to be misled into purchasing this book for the reasons I have given and because editorial reviews are just sugar-coated publisher sales pitches that are also very misleading I continue to warn amazon readers not to waste their money on this book!